{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "IPython.notebook.set_autosave_interval(60000)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Autosaving every 60 seconds\n"
     ]
    }
   ],
   "source": [
    "%autosave 60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "## importing the libraries\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from custom_packages import random_csv_selector\n",
    "from custom_packages.preprocess import data_preprocessor\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import os\n",
    "from statsmodels.graphics.tsaplots import plot_acf\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "import scipy.stats as sps\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = (10, 10)\n",
    "pd.set_option(\"display.max_rows\", 100)\n",
    "pd.set_option(\"display.max_columns\", 200)\n",
    "sns.set_style(\"darkgrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"EDA_Data_01.csv\")\n",
    "data_2 = pd.read_csv(\"/home/CWSHPMU2316/Desktop/EVRangePrediction/data/raw/352891066262722_2019-01-08_cb.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>index</th>\n",
       "      <th>mo</th>\n",
       "      <th>tp</th>\n",
       "      <th>dr</th>\n",
       "      <th>ln</th>\n",
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       "      <th>sp</th>\n",
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       "      <th>mt</th>\n",
       "      <th>mh</th>\n",
       "      <th>ml</th>\n",
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       "      <th>SpeedLimit</th>\n",
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       "      <th>IMEI</th>\n",
       "      <th>trip_id</th>\n",
       "      <th>EVTmg</th>\n",
       "      <th>EVVer</th>\n",
       "      <th>EVCfg</th>\n",
       "      <th>EVIGS</th>\n",
       "      <th>EVIGC</th>\n",
       "      <th>EVVSP</th>\n",
       "      <th>EVDRG</th>\n",
       "      <th>EVGPO</th>\n",
       "      <th>EVBRP</th>\n",
       "      <th>EVCFN</th>\n",
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       "      <th>EVDR1</th>\n",
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       "      <th>EVBAP_Latest</th>\n",
       "      <th>EVBAP_Max</th>\n",
       "      <th>EVBAP_Min</th>\n",
       "      <th>EVCCS</th>\n",
       "      <th>EVCM1</th>\n",
       "      <th>EVCTM</th>\n",
       "      <th>EVCCU</th>\n",
       "      <th>EVCSD</th>\n",
       "      <th>EVVCE</th>\n",
       "      <th>EVPSC_Latest</th>\n",
       "      <th>EVPSC_Max</th>\n",
       "      <th>EVPSC_Min</th>\n",
       "      <th>EVVOU</th>\n",
       "      <th>EVCOU</th>\n",
       "      <th>EVCPV</th>\n",
       "      <th>EVVCD</th>\n",
       "      <th>EVCCD</th>\n",
       "      <th>EVCSC</th>\n",
       "      <th>EVEST</th>\n",
       "      <th>EVCHS</th>\n",
       "      <th>EVR10</th>\n",
       "      <th>EVRMN</th>\n",
       "      <th>EVHVS</th>\n",
       "      <th>EVV12</th>\n",
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       "      <th>EVPWA_MIN</th>\n",
       "      <th>EVMCV_MAX</th>\n",
       "      <th>EVMCV_MIN</th>\n",
       "      <th>EVSMA_MAX</th>\n",
       "      <th>EVSMA_MIN</th>\n",
       "      <th>EVSMI_MAX</th>\n",
       "      <th>EVSMI_MIN</th>\n",
       "      <th>EVSOH</th>\n",
       "      <th>EVBMA_Latest</th>\n",
       "      <th>EVBMA_Max</th>\n",
       "      <th>EVBMA_Min</th>\n",
       "      <th>EVBMI_Latest</th>\n",
       "      <th>EVBMI_Max</th>\n",
       "      <th>EVBMI_Min</th>\n",
       "      <th>EVBOA_AVG</th>\n",
       "      <th>EVBOA_MAX</th>\n",
       "      <th>EVBOA_MIN</th>\n",
       "      <th>EVBOV_AVG</th>\n",
       "      <th>EVBOV_MAX</th>\n",
       "      <th>EVBOV_MIN</th>\n",
       "      <th>EVIRP</th>\n",
       "      <th>EVIRN</th>\n",
       "      <th>EVSOMA</th>\n",
       "      <th>EVSOMI</th>\n",
       "      <th>EVIGM_Latest</th>\n",
       "      <th>EVIGM_Max</th>\n",
       "      <th>EVIGM_Min</th>\n",
       "      <th>EVCOM_Latest</th>\n",
       "      <th>EVCOM_Max</th>\n",
       "      <th>EVCOM_Min</th>\n",
       "      <th>EVICO_Latest</th>\n",
       "      <th>EVICO_Max</th>\n",
       "      <th>EVICO_Min</th>\n",
       "      <th>EVIRT_Latest</th>\n",
       "      <th>EVIRT_Max</th>\n",
       "      <th>EVIRT_Min</th>\n",
       "      <th>EVIDC</th>\n",
       "      <th>EVMGT</th>\n",
       "      <th>EVMGS</th>\n",
       "      <th>EVMGF</th>\n",
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       "      <th>EVCPF_Max</th>\n",
       "      <th>EVCPF_Min</th>\n",
       "      <th>EVCI1_Latest</th>\n",
       "      <th>EVCI1_Max</th>\n",
       "      <th>EVCI1_Min</th>\n",
       "      <th>EVCI2_Latest</th>\n",
       "      <th>EVCI2_Max</th>\n",
       "      <th>EVCI2_Min</th>\n",
       "      <th>EVCBD_Latest</th>\n",
       "      <th>EVCBD_Max</th>\n",
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       "      <th>EVACV_Min</th>\n",
       "      <th>EVCDO_AVG</th>\n",
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       "      <th>EVCDO_Min</th>\n",
       "      <th>EVCCO_AVG</th>\n",
       "      <th>EVCCO_Max</th>\n",
       "      <th>EVCCO_Min</th>\n",
       "      <th>EVSDT</th>\n",
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       "      <th>EVACO_Z</th>\n",
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       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>13.8</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.75</td>\n",
       "      <td>26.1</td>\n",
       "      <td>0</td>\n",
       "      <td>-65532</td>\n",
       "      <td>0</td>\n",
       "      <td>2217</td>\n",
       "      <td>15.5</td>\n",
       "      <td>1</td>\n",
       "      <td>14.9</td>\n",
       "      <td>4.0</td>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3920</td>\n",
       "      <td>3920</td>\n",
       "      <td>3920</td>\n",
       "      <td>138</td>\n",
       "      <td>0</td>\n",
       "      <td>684.0</td>\n",
       "      <td>-789.0</td>\n",
       "      <td>-135.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>DEFREG:352891066263282</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77.072211</td>\n",
       "      <td>28.497356</td>\n",
       "      <td>2.0</td>\n",
       "      <td>200</td>\n",
       "      <td>1543193200580</td>\n",
       "      <td>77.072211</td>\n",
       "      <td>28.497356</td>\n",
       "      <td>88.0052</td>\n",
       "      <td>1104361843</td>\n",
       "      <td>5</td>\n",
       "      <td>30.0</td>\n",
       "      <td>16.3</td>\n",
       "      <td>3</td>\n",
       "      <td>352891066263282</td>\n",
       "      <td>Trip not started</td>\n",
       "      <td>P</td>\n",
       "      <td>M1_POCEV.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1191</td>\n",
       "      <td>0.0</td>\n",
       "      <td>78</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>13.9</td>\n",
       "      <td>0</td>\n",
       "      <td>58.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>13.7</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.25</td>\n",
       "      <td>4.01</td>\n",
       "      <td>4.01</td>\n",
       "      <td>89.8</td>\n",
       "      <td>89.8</td>\n",
       "      <td>89.3</td>\n",
       "      <td>89.3</td>\n",
       "      <td>100</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>17.5</td>\n",
       "      <td>17.5</td>\n",
       "      <td>17.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>8.75</td>\n",
       "      <td>5.5</td>\n",
       "      <td>264.5</td>\n",
       "      <td>255.25</td>\n",
       "      <td>255.0</td>\n",
       "      <td>1810.0</td>\n",
       "      <td>1810</td>\n",
       "      <td>100.0</td>\n",
       "      <td>99.9</td>\n",
       "      <td>16</td>\n",
       "      <td>205</td>\n",
       "      <td>58</td>\n",
       "      <td>17</td>\n",
       "      <td>183</td>\n",
       "      <td>-48</td>\n",
       "      <td>67</td>\n",
       "      <td>155</td>\n",
       "      <td>71</td>\n",
       "      <td>17</td>\n",
       "      <td>188</td>\n",
       "      <td>-38</td>\n",
       "      <td>512.0</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>266</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>13.8</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.75</td>\n",
       "      <td>26.1</td>\n",
       "      <td>0</td>\n",
       "      <td>-65532</td>\n",
       "      <td>0</td>\n",
       "      <td>2217</td>\n",
       "      <td>15.5</td>\n",
       "      <td>1</td>\n",
       "      <td>14.9</td>\n",
       "      <td>4.0</td>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3920</td>\n",
       "      <td>3920</td>\n",
       "      <td>3920</td>\n",
       "      <td>138</td>\n",
       "      <td>0</td>\n",
       "      <td>684.0</td>\n",
       "      <td>-789.0</td>\n",
       "      <td>-135.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>DEFREG:352891066263282</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77.072211</td>\n",
       "      <td>28.497356</td>\n",
       "      <td>2.0</td>\n",
       "      <td>300</td>\n",
       "      <td>1543193200680</td>\n",
       "      <td>77.072211</td>\n",
       "      <td>28.497356</td>\n",
       "      <td>88.0052</td>\n",
       "      <td>1104361843</td>\n",
       "      <td>5</td>\n",
       "      <td>30.0</td>\n",
       "      <td>16.3</td>\n",
       "      <td>3</td>\n",
       "      <td>352891066263282</td>\n",
       "      <td>Trip not started</td>\n",
       "      <td>P</td>\n",
       "      <td>M1_POCEV.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1191</td>\n",
       "      <td>0.0</td>\n",
       "      <td>78</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>13.9</td>\n",
       "      <td>0</td>\n",
       "      <td>58.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>13.7</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.25</td>\n",
       "      <td>4.01</td>\n",
       "      <td>4.01</td>\n",
       "      <td>89.8</td>\n",
       "      <td>89.8</td>\n",
       "      <td>89.3</td>\n",
       "      <td>89.3</td>\n",
       "      <td>100</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>17.5</td>\n",
       "      <td>17.5</td>\n",
       "      <td>17.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>8.75</td>\n",
       "      <td>5.5</td>\n",
       "      <td>264.5</td>\n",
       "      <td>255.25</td>\n",
       "      <td>255.0</td>\n",
       "      <td>1810.0</td>\n",
       "      <td>1810</td>\n",
       "      <td>100.0</td>\n",
       "      <td>99.9</td>\n",
       "      <td>16</td>\n",
       "      <td>205</td>\n",
       "      <td>58</td>\n",
       "      <td>17</td>\n",
       "      <td>183</td>\n",
       "      <td>-48</td>\n",
       "      <td>67</td>\n",
       "      <td>155</td>\n",
       "      <td>71</td>\n",
       "      <td>17</td>\n",
       "      <td>188</td>\n",
       "      <td>-38</td>\n",
       "      <td>512.0</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>266</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>13.8</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.75</td>\n",
       "      <td>26.1</td>\n",
       "      <td>0</td>\n",
       "      <td>-65532</td>\n",
       "      <td>0</td>\n",
       "      <td>2217</td>\n",
       "      <td>15.5</td>\n",
       "      <td>1</td>\n",
       "      <td>14.9</td>\n",
       "      <td>4.0</td>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3920</td>\n",
       "      <td>3920</td>\n",
       "      <td>3920</td>\n",
       "      <td>138</td>\n",
       "      <td>0</td>\n",
       "      <td>684.0</td>\n",
       "      <td>-789.0</td>\n",
       "      <td>-135.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>DEFREG:352891066263282</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77.072211</td>\n",
       "      <td>28.497356</td>\n",
       "      <td>2.0</td>\n",
       "      <td>400</td>\n",
       "      <td>1543193200780</td>\n",
       "      <td>77.072211</td>\n",
       "      <td>28.497356</td>\n",
       "      <td>88.0052</td>\n",
       "      <td>1104361843</td>\n",
       "      <td>5</td>\n",
       "      <td>30.0</td>\n",
       "      <td>16.3</td>\n",
       "      <td>3</td>\n",
       "      <td>352891066263282</td>\n",
       "      <td>Trip not started</td>\n",
       "      <td>P</td>\n",
       "      <td>M1_POCEV.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1191</td>\n",
       "      <td>0.0</td>\n",
       "      <td>78</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>13.9</td>\n",
       "      <td>0</td>\n",
       "      <td>58.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>13.7</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.25</td>\n",
       "      <td>4.01</td>\n",
       "      <td>4.01</td>\n",
       "      <td>89.8</td>\n",
       "      <td>89.8</td>\n",
       "      <td>89.3</td>\n",
       "      <td>89.3</td>\n",
       "      <td>100</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>17.5</td>\n",
       "      <td>17.5</td>\n",
       "      <td>17.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>8.75</td>\n",
       "      <td>5.5</td>\n",
       "      <td>264.5</td>\n",
       "      <td>255.25</td>\n",
       "      <td>255.0</td>\n",
       "      <td>1810.0</td>\n",
       "      <td>1810</td>\n",
       "      <td>100.0</td>\n",
       "      <td>99.9</td>\n",
       "      <td>16</td>\n",
       "      <td>205</td>\n",
       "      <td>58</td>\n",
       "      <td>17</td>\n",
       "      <td>183</td>\n",
       "      <td>-48</td>\n",
       "      <td>67</td>\n",
       "      <td>155</td>\n",
       "      <td>71</td>\n",
       "      <td>17</td>\n",
       "      <td>188</td>\n",
       "      <td>-38</td>\n",
       "      <td>512.0</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>266</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>13.8</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.75</td>\n",
       "      <td>26.1</td>\n",
       "      <td>0</td>\n",
       "      <td>-65532</td>\n",
       "      <td>0</td>\n",
       "      <td>2217</td>\n",
       "      <td>15.5</td>\n",
       "      <td>1</td>\n",
       "      <td>14.9</td>\n",
       "      <td>4.0</td>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3920</td>\n",
       "      <td>3920</td>\n",
       "      <td>3920</td>\n",
       "      <td>138</td>\n",
       "      <td>0</td>\n",
       "      <td>684.0</td>\n",
       "      <td>-789.0</td>\n",
       "      <td>-135.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  index                      mo  tp  dr         ln         lt  \\\n",
       "0           0      0  DEFREG:352891066263282   1 NaN  77.072211  28.497356   \n",
       "1           1      1  DEFREG:352891066263282   1 NaN  77.072211  28.497356   \n",
       "2           2      2  DEFREG:352891066263282   1 NaN  77.072211  28.497356   \n",
       "3           3      3  DEFREG:352891066263282   1 NaN  77.072211  28.497356   \n",
       "4           4      4  DEFREG:352891066263282   1 NaN  77.072211  28.497356   \n",
       "\n",
       "    hd   sp             tm         mn         mt       mh          ml  mr  \\\n",
       "0  2.0    0  1543193200380  77.072211  28.497356  88.0052  1104361843   5   \n",
       "1  2.0  100  1543193200480  77.072211  28.497356  88.0052  1104361843   5   \n",
       "2  2.0  200  1543193200580  77.072211  28.497356  88.0052  1104361843   5   \n",
       "3  2.0  300  1543193200680  77.072211  28.497356  88.0052  1104361843   5   \n",
       "4  2.0  400  1543193200780  77.072211  28.497356  88.0052  1104361843   5   \n",
       "\n",
       "   SpeedLimit  hdop  numsat             IMEI           trip_id EVTmg  \\\n",
       "0        30.0  16.3       3  352891066263282  Trip not started     P   \n",
       "1        30.0  16.3       3  352891066263282  Trip not started     P   \n",
       "2        30.0  16.3       3  352891066263282  Trip not started     P   \n",
       "3        30.0  16.3       3  352891066263282  Trip not started     P   \n",
       "4        30.0  16.3       3  352891066263282  Trip not started     P   \n",
       "\n",
       "        EVVer  EVCfg  EVIGS  EVIGC  EVVSP  EVDRG  EVGPO  EVBRP  EVCFN  EVICR  \\\n",
       "0  M1_POCEV.0    NaN      1   1191    0.0     78     10      0      0      1   \n",
       "1  M1_POCEV.0    NaN      1   1191    0.0     78     10      0      0      1   \n",
       "2  M1_POCEV.0    NaN      1   1191    0.0     78     10      0      0      1   \n",
       "3  M1_POCEV.0    NaN      1   1191    0.0     78     10      0      0      1   \n",
       "4  M1_POCEV.0    NaN      1   1191    0.0     78     10      0      0      1   \n",
       "\n",
       "   EVTRQ  EVCST  EVDDC  EVBCA  EVBBV  EVDR1  EVDR2  EVRGT  EVACP  \\\n",
       "0    0.0      1   13.9      0   58.5      0      0      0    0.0   \n",
       "1    0.0      1   13.9      0   58.5      0      0      0    0.0   \n",
       "2    0.0      1   13.9      0   58.5      0      0      0    0.0   \n",
       "3    0.0      1   13.9      0   58.5      0      0      0    0.0   \n",
       "4    0.0      1   13.9      0   58.5      0      0      0    0.0   \n",
       "\n",
       "   EVBAP_Latest  EVBAP_Max  EVBAP_Min  EVCCS  EVCM1  EVCTM  EVCCU  EVCSD  \\\n",
       "0             0          0          0      0    NaN    NaN    NaN    NaN   \n",
       "1             0          0          0      0    NaN    NaN    NaN    NaN   \n",
       "2             0          0          0      0    NaN    NaN    NaN    NaN   \n",
       "3             0          0          0      0    NaN    NaN    NaN    NaN   \n",
       "4             0          0          0      0    NaN    NaN    NaN    NaN   \n",
       "\n",
       "   EVVCE  EVPSC_Latest  EVPSC_Max  EVPSC_Min  EVVOU  EVCOU  EVCPV  EVVCD  \\\n",
       "0    NaN           NaN        NaN        NaN    NaN    NaN    NaN    NaN   \n",
       "1    NaN           NaN        NaN        NaN    NaN    NaN    NaN    NaN   \n",
       "2    NaN           NaN        NaN        NaN    NaN    NaN    NaN    NaN   \n",
       "3    NaN           NaN        NaN        NaN    NaN    NaN    NaN    NaN   \n",
       "4    NaN           NaN        NaN        NaN    NaN    NaN    NaN    NaN   \n",
       "\n",
       "   EVCCD  EVCSC  EVEST  EVCHS  EVR10  EVRMN  EVHVS  EVV12  EVPWA_MAX  \\\n",
       "0    NaN    NaN    NaN    NaN    NaN    NaN     10   13.7       0.25   \n",
       "1    NaN    NaN    NaN    NaN    NaN    NaN     10   13.7       0.25   \n",
       "2    NaN    NaN    NaN    NaN    NaN    NaN     10   13.7       0.25   \n",
       "3    NaN    NaN    NaN    NaN    NaN    NaN     10   13.7       0.25   \n",
       "4    NaN    NaN    NaN    NaN    NaN    NaN     10   13.7       0.25   \n",
       "\n",
       "   EVPWA_MIN  EVMCV_MAX  EVMCV_MIN  EVSMA_MAX  EVSMA_MIN  EVSMI_MAX  \\\n",
       "0       0.25       4.01       4.01       89.8       89.8       89.3   \n",
       "1       0.25       4.01       4.01       89.8       89.8       89.3   \n",
       "2       0.25       4.01       4.01       89.8       89.8       89.3   \n",
       "3       0.25       4.01       4.01       89.8       89.8       89.3   \n",
       "4       0.25       4.01       4.01       89.8       89.8       89.3   \n",
       "\n",
       "   EVSMI_MIN  EVSOH  EVBMA_Latest  EVBMA_Max  EVBMA_Min  EVBMI_Latest  \\\n",
       "0       89.3    100          18.0       18.0       18.0          17.5   \n",
       "1       89.3    100          18.0       18.0       18.0          17.5   \n",
       "2       89.3    100          18.0       18.0       18.0          17.5   \n",
       "3       89.3    100          18.0       18.0       18.0          17.5   \n",
       "4       89.3    100          18.0       18.0       18.0          17.5   \n",
       "\n",
       "   EVBMI_Max  EVBMI_Min  EVBOA_AVG  EVBOA_MAX  EVBOA_MIN  EVBOV_AVG  \\\n",
       "0       17.5       17.5        0.5       8.75        5.5      264.5   \n",
       "1       17.5       17.5        0.5       8.75        5.5      264.5   \n",
       "2       17.5       17.5        0.5       8.75        5.5      264.5   \n",
       "3       17.5       17.5        0.5       8.75        5.5      264.5   \n",
       "4       17.5       17.5        0.5       8.75        5.5      264.5   \n",
       "\n",
       "   EVBOV_MAX  EVBOV_MIN   EVIRP  EVIRN  EVSOMA  EVSOMI  EVIGM_Latest  \\\n",
       "0     255.25      255.0  1810.0   1810   100.0    99.9            16   \n",
       "1     255.25      255.0  1810.0   1810   100.0    99.9            16   \n",
       "2     255.25      255.0  1810.0   1810   100.0    99.9            16   \n",
       "3     255.25      255.0  1810.0   1810   100.0    99.9            16   \n",
       "4     255.25      255.0  1810.0   1810   100.0    99.9            16   \n",
       "\n",
       "   EVIGM_Max  EVIGM_Min  EVCOM_Latest  EVCOM_Max  EVCOM_Min  EVICO_Latest  \\\n",
       "0        205         58            17        183        -48            67   \n",
       "1        205         58            17        183        -48            67   \n",
       "2        205         58            17        183        -48            67   \n",
       "3        205         58            17        183        -48            67   \n",
       "4        205         58            17        183        -48            67   \n",
       "\n",
       "   EVICO_Max  EVICO_Min  EVIRT_Latest  EVIRT_Max  EVIRT_Min  EVIDC  EVMGT  \\\n",
       "0        155         71            17        188        -38  512.0   -0.1   \n",
       "1        155         71            17        188        -38  512.0   -0.1   \n",
       "2        155         71            17        188        -38  512.0   -0.1   \n",
       "3        155         71            17        188        -38  512.0   -0.1   \n",
       "4        155         71            17        188        -38  512.0   -0.1   \n",
       "\n",
       "   EVMGS  EVMGF  EVMGR  EVIND  EVICM  EVCPW  EVCPF_Latest  EVCPF_Max  \\\n",
       "0      0    0.0    0.0    266      3    NaN           NaN        NaN   \n",
       "1      0    0.0    0.0    266      3    NaN           NaN        NaN   \n",
       "2      0    0.0    0.0    266      3    NaN           NaN        NaN   \n",
       "3      0    0.0    0.0    266      3    NaN           NaN        NaN   \n",
       "4      0    0.0    0.0    266      3    NaN           NaN        NaN   \n",
       "\n",
       "   EVCPF_Min  EVCI1_Latest  EVCI1_Max  EVCI1_Min  EVCI2_Latest  EVCI2_Max  \\\n",
       "0        NaN           NaN        NaN        NaN           NaN        NaN   \n",
       "1        NaN           NaN        NaN        NaN           NaN        NaN   \n",
       "2        NaN           NaN        NaN        NaN           NaN        NaN   \n",
       "3        NaN           NaN        NaN        NaN           NaN        NaN   \n",
       "4        NaN           NaN        NaN        NaN           NaN        NaN   \n",
       "\n",
       "   EVCI2_Min  EVCBD_Latest  EVCBD_Max  EVCBD_Min  EVCRP  EVACV_AVG  EVACV_Max  \\\n",
       "0        NaN           NaN        NaN        NaN      1        NaN        NaN   \n",
       "1        NaN           NaN        NaN        NaN      1        NaN        NaN   \n",
       "2        NaN           NaN        NaN        NaN      1        NaN        NaN   \n",
       "3        NaN           NaN        NaN        NaN      1        NaN        NaN   \n",
       "4        NaN           NaN        NaN        NaN      1        NaN        NaN   \n",
       "\n",
       "   EVACV_Min  EVCDO_AVG  EVCDO_Max  EVCDO_Min  EVCCO_AVG  EVCCO_Max  \\\n",
       "0        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "1        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "3        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "4        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "   EVCCO_Min  EVSDT  EVCHC  EVCHT_AVG  EVCHT_Max  EVCHT_Min  EVCHE  EVDI1  \\\n",
       "0        NaN    NaN    NaN        NaN        NaN        NaN    NaN     17   \n",
       "1        NaN    NaN    NaN        NaN        NaN        NaN    NaN     17   \n",
       "2        NaN    NaN    NaN        NaN        NaN        NaN    NaN     17   \n",
       "3        NaN    NaN    NaN        NaN        NaN        NaN    NaN     17   \n",
       "4        NaN    NaN    NaN        NaN        NaN        NaN    NaN     17   \n",
       "\n",
       "   EVDI2  EVDIT  EVOII  EVDOA  EVDOV  EVDSE  EVACS  EVPSS  EVMSC  EVRER  \\\n",
       "0     16     16      0     39   13.8      0    0.0  34.75   26.1      0   \n",
       "1     16     16      0     39   13.8      0    0.0  34.75   26.1      0   \n",
       "2     16     16      0     39   13.8      0    0.0  34.75   26.1      0   \n",
       "3     16     16      0     39   13.8      0    0.0  34.75   26.1      0   \n",
       "4     16     16      0     39   13.8      0    0.0  34.75   26.1      0   \n",
       "\n",
       "   EVDRV  EVMTR  EVODO  EVOAS  EVHTR  EVACE  EVTRE  EVCLC  EVBFN  EVIST  \\\n",
       "0 -65532      0   2217   15.5      1   14.9    4.0     65      1      0   \n",
       "1 -65532      0   2217   15.5      1   14.9    4.0     65      1      0   \n",
       "2 -65532      0   2217   15.5      1   14.9    4.0     65      1      0   \n",
       "3 -65532      0   2217   15.5      1   14.9    4.0     65      1      0   \n",
       "4 -65532      0   2217   15.5      1   14.9    4.0     65      1      0   \n",
       "\n",
       "   EVHTP_AVG  EVHTP_Max  EVHTP_Min  EVVBT  EVGSM  EVACO_X  EVACO_Y  EVACO_Z  \\\n",
       "0       3920       3920       3920    138      0    684.0   -789.0   -135.0   \n",
       "1       3920       3920       3920    138      0    684.0   -789.0   -135.0   \n",
       "2       3920       3920       3920    138      0    684.0   -789.0   -135.0   \n",
       "3       3920       3920       3920    138      0    684.0   -789.0   -135.0   \n",
       "4       3920       3920       3920    138      0    684.0   -789.0   -135.0   \n",
       "\n",
       "   Unnamed: 164  \n",
       "0           NaN  \n",
       "1           NaN  \n",
       "2           NaN  \n",
       "3           NaN  \n",
       "4           NaN  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['DEFREG:352891066263282', 'DEFREG:352891066262722',\n",
       "       'DEFREG:358272088699072'], dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.mo.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mo</th>\n",
       "      <th>tp</th>\n",
       "      <th>dr</th>\n",
       "      <th>ln</th>\n",
       "      <th>lt</th>\n",
       "      <th>hd</th>\n",
       "      <th>sp</th>\n",
       "      <th>tm</th>\n",
       "      <th>mn</th>\n",
       "      <th>mt</th>\n",
       "      <th>mh</th>\n",
       "      <th>ml</th>\n",
       "      <th>mr</th>\n",
       "      <th>SpeedLimit</th>\n",
       "      <th>hdop</th>\n",
       "      <th>numsat</th>\n",
       "      <th>IMEI</th>\n",
       "      <th>trip_id</th>\n",
       "      <th>EVTmg</th>\n",
       "      <th>EVVer</th>\n",
       "      <th>EVCfg</th>\n",
       "      <th>EVIGS</th>\n",
       "      <th>EVIGC</th>\n",
       "      <th>EVVSP</th>\n",
       "      <th>EVDRG</th>\n",
       "      <th>EVGPO</th>\n",
       "      <th>EVBRP</th>\n",
       "      <th>EVCFN</th>\n",
       "      <th>EVICR</th>\n",
       "      <th>EVTRQ</th>\n",
       "      <th>EVCST</th>\n",
       "      <th>EVDDC</th>\n",
       "      <th>EVBCA</th>\n",
       "      <th>EVBBV</th>\n",
       "      <th>EVDR1</th>\n",
       "      <th>EVDR2</th>\n",
       "      <th>EVRGT</th>\n",
       "      <th>EVACP</th>\n",
       "      <th>EVBAP_Latest</th>\n",
       "      <th>EVBAP_Max</th>\n",
       "      <th>EVBAP_Min</th>\n",
       "      <th>EVCCS</th>\n",
       "      <th>EVCM1</th>\n",
       "      <th>EVCTM</th>\n",
       "      <th>EVCCU</th>\n",
       "      <th>EVCSD</th>\n",
       "      <th>EVVCE</th>\n",
       "      <th>EVPSC_Latest</th>\n",
       "      <th>EVPSC_Max</th>\n",
       "      <th>EVPSC_Min</th>\n",
       "      <th>EVVOU</th>\n",
       "      <th>EVCOU</th>\n",
       "      <th>EVCPV</th>\n",
       "      <th>EVVCD</th>\n",
       "      <th>EVCCD</th>\n",
       "      <th>EVCSC</th>\n",
       "      <th>EVEST</th>\n",
       "      <th>EVCHS</th>\n",
       "      <th>EVR10</th>\n",
       "      <th>EVRMN</th>\n",
       "      <th>EVHVS</th>\n",
       "      <th>EVV12</th>\n",
       "      <th>EVPWA_MAX</th>\n",
       "      <th>EVPWA_MIN</th>\n",
       "      <th>EVMCV_MAX</th>\n",
       "      <th>EVMCV_MIN</th>\n",
       "      <th>EVSMA_MAX</th>\n",
       "      <th>EVSMA_MIN</th>\n",
       "      <th>EVSMI_MAX</th>\n",
       "      <th>EVSMI_MIN</th>\n",
       "      <th>EVSOH</th>\n",
       "      <th>EVBMA_Latest</th>\n",
       "      <th>EVBMA_Max</th>\n",
       "      <th>EVBMA_Min</th>\n",
       "      <th>EVBMI_Latest</th>\n",
       "      <th>EVBMI_Max</th>\n",
       "      <th>EVBMI_Min</th>\n",
       "      <th>EVBOA_AVG</th>\n",
       "      <th>EVBOA_MAX</th>\n",
       "      <th>EVBOA_MIN</th>\n",
       "      <th>EVBOV_AVG</th>\n",
       "      <th>EVBOV_MAX</th>\n",
       "      <th>EVBOV_MIN</th>\n",
       "      <th>EVIRP</th>\n",
       "      <th>EVIRN</th>\n",
       "      <th>EVSOMA</th>\n",
       "      <th>EVSOMI</th>\n",
       "      <th>EVIGM_Latest</th>\n",
       "      <th>EVIGM_Max</th>\n",
       "      <th>EVIGM_Min</th>\n",
       "      <th>EVCOM_Latest</th>\n",
       "      <th>EVCOM_Max</th>\n",
       "      <th>EVCOM_Min</th>\n",
       "      <th>EVICO_Latest</th>\n",
       "      <th>EVICO_Max</th>\n",
       "      <th>EVICO_Min</th>\n",
       "      <th>EVIRT_Latest</th>\n",
       "      <th>EVIRT_Max</th>\n",
       "      <th>EVIRT_Min</th>\n",
       "      <th>EVIDC</th>\n",
       "      <th>EVMGT</th>\n",
       "      <th>EVMGS</th>\n",
       "      <th>EVMGF</th>\n",
       "      <th>EVMGR</th>\n",
       "      <th>EVIND</th>\n",
       "      <th>EVICM</th>\n",
       "      <th>EVCPW</th>\n",
       "      <th>EVCPF_Latest</th>\n",
       "      <th>EVCPF_Max</th>\n",
       "      <th>EVCPF_Min</th>\n",
       "      <th>EVCI1_Latest</th>\n",
       "      <th>EVCI1_Max</th>\n",
       "      <th>EVCI1_Min</th>\n",
       "      <th>EVCI2_Latest</th>\n",
       "      <th>EVCI2_Max</th>\n",
       "      <th>EVCI2_Min</th>\n",
       "      <th>EVCBD_Latest</th>\n",
       "      <th>EVCBD_Max</th>\n",
       "      <th>EVCBD_Min</th>\n",
       "      <th>EVCRP</th>\n",
       "      <th>EVACV_AVG</th>\n",
       "      <th>EVACV_Max</th>\n",
       "      <th>EVACV_Min</th>\n",
       "      <th>EVCDO_AVG</th>\n",
       "      <th>EVCDO_Max</th>\n",
       "      <th>EVCDO_Min</th>\n",
       "      <th>EVCCO_AVG</th>\n",
       "      <th>EVCCO_Max</th>\n",
       "      <th>EVCCO_Min</th>\n",
       "      <th>EVSDT</th>\n",
       "      <th>EVCHC</th>\n",
       "      <th>EVCHT_AVG</th>\n",
       "      <th>EVCHT_Max</th>\n",
       "      <th>EVCHT_Min</th>\n",
       "      <th>EVCHE</th>\n",
       "      <th>EVDI1</th>\n",
       "      <th>EVDI2</th>\n",
       "      <th>EVDIT</th>\n",
       "      <th>EVOII</th>\n",
       "      <th>EVDOA</th>\n",
       "      <th>EVDOV</th>\n",
       "      <th>EVDSE</th>\n",
       "      <th>EVACS</th>\n",
       "      <th>EVPSS</th>\n",
       "      <th>EVMSC</th>\n",
       "      <th>EVRER</th>\n",
       "      <th>EVDRV</th>\n",
       "      <th>EVMTR</th>\n",
       "      <th>EVODO</th>\n",
       "      <th>EVOAS</th>\n",
       "      <th>EVHTR</th>\n",
       "      <th>EVACE</th>\n",
       "      <th>EVTRE</th>\n",
       "      <th>EVCLC</th>\n",
       "      <th>EVBFN</th>\n",
       "      <th>EVIST</th>\n",
       "      <th>EVHTP_AVG</th>\n",
       "      <th>EVHTP_Max</th>\n",
       "      <th>EVHTP_Min</th>\n",
       "      <th>EVVBT</th>\n",
       "      <th>EVGSM</th>\n",
       "      <th>EVACO_X</th>\n",
       "      <th>EVACO_Y</th>\n",
       "      <th>EVACO_Z</th>\n",
       "      <th>Unnamed: 164</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DEFREG:352891066262722</td>\n",
       "      <td>Trip not started</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77.07351</td>\n",
       "      <td>28.49696</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1546918001380</td>\n",
       "      <td>77.073615</td>\n",
       "      <td>28.496959</td>\n",
       "      <td>0.657325</td>\n",
       "      <td>714359872</td>\n",
       "      <td>5</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>7</td>\n",
       "      <td>352891066262722</td>\n",
       "      <td>Trip not started</td>\n",
       "      <td>P</td>\n",
       "      <td>M1_POCEV.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>877</td>\n",
       "      <td>0.0</td>\n",
       "      <td>87</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>13.9</td>\n",
       "      <td>0</td>\n",
       "      <td>48.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>13.8</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.03</td>\n",
       "      <td>4.03</td>\n",
       "      <td>92.9</td>\n",
       "      <td>92.9</td>\n",
       "      <td>92.6</td>\n",
       "      <td>92.6</td>\n",
       "      <td>100</td>\n",
       "      <td>15.5</td>\n",
       "      <td>15.5</td>\n",
       "      <td>15.5</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>-512.0</td>\n",
       "      <td>-328.0</td>\n",
       "      <td>267.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>16198.0</td>\n",
       "      <td>1830</td>\n",
       "      <td>1830</td>\n",
       "      <td>99.9</td>\n",
       "      <td>99.9</td>\n",
       "      <td>11</td>\n",
       "      <td>-50</td>\n",
       "      <td>-38</td>\n",
       "      <td>11</td>\n",
       "      <td>166</td>\n",
       "      <td>203</td>\n",
       "      <td>11</td>\n",
       "      <td>-50</td>\n",
       "      <td>-41</td>\n",
       "      <td>11</td>\n",
       "      <td>172</td>\n",
       "      <td>213</td>\n",
       "      <td>512.0</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>270</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>13.9</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.125</td>\n",
       "      <td>-279.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0</td>\n",
       "      <td>-65532</td>\n",
       "      <td>0</td>\n",
       "      <td>4067</td>\n",
       "      <td>11.5</td>\n",
       "      <td>1</td>\n",
       "      <td>10.7</td>\n",
       "      <td>4</td>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4000</td>\n",
       "      <td>4000</td>\n",
       "      <td>4000</td>\n",
       "      <td>137</td>\n",
       "      <td>0</td>\n",
       "      <td>-733.0</td>\n",
       "      <td>731.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DEFREG:352891066262722</td>\n",
       "      <td>Trip not started</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77.07351</td>\n",
       "      <td>28.49696</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1546918001480</td>\n",
       "      <td>77.073615</td>\n",
       "      <td>28.496959</td>\n",
       "      <td>0.657325</td>\n",
       "      <td>714359872</td>\n",
       "      <td>5</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>7</td>\n",
       "      <td>352891066262722</td>\n",
       "      <td>Trip not started</td>\n",
       "      <td>P</td>\n",
       "      <td>M1_POCEV.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>877</td>\n",
       "      <td>0.0</td>\n",
       "      <td>87</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>13.9</td>\n",
       "      <td>0</td>\n",
       "      <td>48.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>13.8</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.03</td>\n",
       "      <td>4.03</td>\n",
       "      <td>92.9</td>\n",
       "      <td>92.9</td>\n",
       "      <td>92.6</td>\n",
       "      <td>92.6</td>\n",
       "      <td>100</td>\n",
       "      <td>15.5</td>\n",
       "      <td>15.5</td>\n",
       "      <td>15.5</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>-512.0</td>\n",
       "      <td>-328.0</td>\n",
       "      <td>267.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>16198.0</td>\n",
       "      <td>1830</td>\n",
       "      <td>1830</td>\n",
       "      <td>99.9</td>\n",
       "      <td>99.9</td>\n",
       "      <td>11</td>\n",
       "      <td>-50</td>\n",
       "      <td>-38</td>\n",
       "      <td>11</td>\n",
       "      <td>166</td>\n",
       "      <td>203</td>\n",
       "      <td>11</td>\n",
       "      <td>-50</td>\n",
       "      <td>-41</td>\n",
       "      <td>11</td>\n",
       "      <td>172</td>\n",
       "      <td>213</td>\n",
       "      <td>512.0</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>270</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>13.9</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.125</td>\n",
       "      <td>-279.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0</td>\n",
       "      <td>-65532</td>\n",
       "      <td>0</td>\n",
       "      <td>4067</td>\n",
       "      <td>11.5</td>\n",
       "      <td>1</td>\n",
       "      <td>10.7</td>\n",
       "      <td>4</td>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4000</td>\n",
       "      <td>4000</td>\n",
       "      <td>4000</td>\n",
       "      <td>137</td>\n",
       "      <td>0</td>\n",
       "      <td>-733.0</td>\n",
       "      <td>731.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DEFREG:352891066262722</td>\n",
       "      <td>Trip not started</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77.07351</td>\n",
       "      <td>28.49696</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1546918001580</td>\n",
       "      <td>77.073615</td>\n",
       "      <td>28.496959</td>\n",
       "      <td>0.657325</td>\n",
       "      <td>714359872</td>\n",
       "      <td>5</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>7</td>\n",
       "      <td>352891066262722</td>\n",
       "      <td>Trip not started</td>\n",
       "      <td>P</td>\n",
       "      <td>M1_POCEV.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>877</td>\n",
       "      <td>0.0</td>\n",
       "      <td>87</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>13.9</td>\n",
       "      <td>0</td>\n",
       "      <td>48.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>13.8</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.03</td>\n",
       "      <td>4.03</td>\n",
       "      <td>92.9</td>\n",
       "      <td>92.9</td>\n",
       "      <td>92.6</td>\n",
       "      <td>92.6</td>\n",
       "      <td>100</td>\n",
       "      <td>15.5</td>\n",
       "      <td>15.5</td>\n",
       "      <td>15.5</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>-512.0</td>\n",
       "      <td>-328.0</td>\n",
       "      <td>267.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>16198.0</td>\n",
       "      <td>1830</td>\n",
       "      <td>1830</td>\n",
       "      <td>99.9</td>\n",
       "      <td>99.9</td>\n",
       "      <td>11</td>\n",
       "      <td>-50</td>\n",
       "      <td>-38</td>\n",
       "      <td>11</td>\n",
       "      <td>166</td>\n",
       "      <td>203</td>\n",
       "      <td>11</td>\n",
       "      <td>-50</td>\n",
       "      <td>-41</td>\n",
       "      <td>11</td>\n",
       "      <td>172</td>\n",
       "      <td>213</td>\n",
       "      <td>512.0</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>270</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>13.9</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.125</td>\n",
       "      <td>-279.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0</td>\n",
       "      <td>-65532</td>\n",
       "      <td>0</td>\n",
       "      <td>4067</td>\n",
       "      <td>11.5</td>\n",
       "      <td>1</td>\n",
       "      <td>10.7</td>\n",
       "      <td>4</td>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4000</td>\n",
       "      <td>4000</td>\n",
       "      <td>4000</td>\n",
       "      <td>137</td>\n",
       "      <td>0</td>\n",
       "      <td>-733.0</td>\n",
       "      <td>731.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DEFREG:352891066262722</td>\n",
       "      <td>Trip not started</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77.07351</td>\n",
       "      <td>28.49696</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1546918001680</td>\n",
       "      <td>77.073615</td>\n",
       "      <td>28.496959</td>\n",
       "      <td>0.657325</td>\n",
       "      <td>714359872</td>\n",
       "      <td>5</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>7</td>\n",
       "      <td>352891066262722</td>\n",
       "      <td>Trip not started</td>\n",
       "      <td>P</td>\n",
       "      <td>M1_POCEV.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>877</td>\n",
       "      <td>0.0</td>\n",
       "      <td>87</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>13.9</td>\n",
       "      <td>0</td>\n",
       "      <td>48.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>13.8</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.03</td>\n",
       "      <td>4.03</td>\n",
       "      <td>92.9</td>\n",
       "      <td>92.9</td>\n",
       "      <td>92.6</td>\n",
       "      <td>92.6</td>\n",
       "      <td>100</td>\n",
       "      <td>15.5</td>\n",
       "      <td>15.5</td>\n",
       "      <td>15.5</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>-512.0</td>\n",
       "      <td>-328.0</td>\n",
       "      <td>267.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>16198.0</td>\n",
       "      <td>1830</td>\n",
       "      <td>1830</td>\n",
       "      <td>99.9</td>\n",
       "      <td>99.9</td>\n",
       "      <td>11</td>\n",
       "      <td>-50</td>\n",
       "      <td>-38</td>\n",
       "      <td>11</td>\n",
       "      <td>166</td>\n",
       "      <td>203</td>\n",
       "      <td>11</td>\n",
       "      <td>-50</td>\n",
       "      <td>-41</td>\n",
       "      <td>11</td>\n",
       "      <td>172</td>\n",
       "      <td>213</td>\n",
       "      <td>512.0</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>270</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>13.9</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.125</td>\n",
       "      <td>-279.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0</td>\n",
       "      <td>-65532</td>\n",
       "      <td>0</td>\n",
       "      <td>4067</td>\n",
       "      <td>11.5</td>\n",
       "      <td>1</td>\n",
       "      <td>10.7</td>\n",
       "      <td>4</td>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4000</td>\n",
       "      <td>4000</td>\n",
       "      <td>4000</td>\n",
       "      <td>137</td>\n",
       "      <td>0</td>\n",
       "      <td>-733.0</td>\n",
       "      <td>731.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DEFREG:352891066262722</td>\n",
       "      <td>Trip not started</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77.07351</td>\n",
       "      <td>28.49696</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1546918001780</td>\n",
       "      <td>77.073615</td>\n",
       "      <td>28.496959</td>\n",
       "      <td>0.657325</td>\n",
       "      <td>714359872</td>\n",
       "      <td>5</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>7</td>\n",
       "      <td>352891066262722</td>\n",
       "      <td>Trip not started</td>\n",
       "      <td>P</td>\n",
       "      <td>M1_POCEV.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>877</td>\n",
       "      <td>0.0</td>\n",
       "      <td>87</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>13.9</td>\n",
       "      <td>0</td>\n",
       "      <td>48.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>13.8</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.03</td>\n",
       "      <td>4.03</td>\n",
       "      <td>92.9</td>\n",
       "      <td>92.9</td>\n",
       "      <td>92.6</td>\n",
       "      <td>92.6</td>\n",
       "      <td>100</td>\n",
       "      <td>15.5</td>\n",
       "      <td>15.5</td>\n",
       "      <td>15.5</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>-512.0</td>\n",
       "      <td>-328.0</td>\n",
       "      <td>267.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>16198.0</td>\n",
       "      <td>1830</td>\n",
       "      <td>1830</td>\n",
       "      <td>99.9</td>\n",
       "      <td>99.9</td>\n",
       "      <td>11</td>\n",
       "      <td>-50</td>\n",
       "      <td>-38</td>\n",
       "      <td>11</td>\n",
       "      <td>166</td>\n",
       "      <td>203</td>\n",
       "      <td>11</td>\n",
       "      <td>-50</td>\n",
       "      <td>-41</td>\n",
       "      <td>11</td>\n",
       "      <td>172</td>\n",
       "      <td>213</td>\n",
       "      <td>512.0</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>270</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>13.9</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.125</td>\n",
       "      <td>-279.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0</td>\n",
       "      <td>-65532</td>\n",
       "      <td>0</td>\n",
       "      <td>4067</td>\n",
       "      <td>11.5</td>\n",
       "      <td>1</td>\n",
       "      <td>10.7</td>\n",
       "      <td>4</td>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4000</td>\n",
       "      <td>4000</td>\n",
       "      <td>4000</td>\n",
       "      <td>137</td>\n",
       "      <td>0</td>\n",
       "      <td>-733.0</td>\n",
       "      <td>731.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       mo                tp  dr        ln        lt  hd  sp  \\\n",
       "0  DEFREG:352891066262722  Trip not started NaN  77.07351  28.49696 NaN NaN   \n",
       "1  DEFREG:352891066262722  Trip not started NaN  77.07351  28.49696 NaN NaN   \n",
       "2  DEFREG:352891066262722  Trip not started NaN  77.07351  28.49696 NaN NaN   \n",
       "3  DEFREG:352891066262722  Trip not started NaN  77.07351  28.49696 NaN NaN   \n",
       "4  DEFREG:352891066262722  Trip not started NaN  77.07351  28.49696 NaN NaN   \n",
       "\n",
       "              tm         mn         mt        mh         ml  mr  SpeedLimit  \\\n",
       "0  1546918001380  77.073615  28.496959  0.657325  714359872   5        30.0   \n",
       "1  1546918001480  77.073615  28.496959  0.657325  714359872   5        30.0   \n",
       "2  1546918001580  77.073615  28.496959  0.657325  714359872   5        30.0   \n",
       "3  1546918001680  77.073615  28.496959  0.657325  714359872   5        30.0   \n",
       "4  1546918001780  77.073615  28.496959  0.657325  714359872   5        30.0   \n",
       "\n",
       "   hdop  numsat             IMEI           trip_id EVTmg       EVVer  EVCfg  \\\n",
       "0   1.1       7  352891066262722  Trip not started     P  M1_POCEV.0    NaN   \n",
       "1   1.1       7  352891066262722  Trip not started     P  M1_POCEV.0    NaN   \n",
       "2   1.1       7  352891066262722  Trip not started     P  M1_POCEV.0    NaN   \n",
       "3   1.1       7  352891066262722  Trip not started     P  M1_POCEV.0    NaN   \n",
       "4   1.1       7  352891066262722  Trip not started     P  M1_POCEV.0    NaN   \n",
       "\n",
       "   EVIGS  EVIGC  EVVSP  EVDRG  EVGPO  EVBRP  EVCFN  EVICR  EVTRQ  EVCST  \\\n",
       "0      1    877    0.0     87     10      0      0      1    0.0      1   \n",
       "1      1    877    0.0     87     10      0      0      1    0.0      1   \n",
       "2      1    877    0.0     87     10      0      0      1    0.0      1   \n",
       "3      1    877    0.0     87     10      0      0      1    0.0      1   \n",
       "4      1    877    0.0     87     10      0      0      1    0.0      1   \n",
       "\n",
       "   EVDDC  EVBCA  EVBBV  EVDR1  EVDR2  EVRGT  EVACP  EVBAP_Latest  EVBAP_Max  \\\n",
       "0   13.9      0   48.5      0      0      0    0.0             0          0   \n",
       "1   13.9      0   48.5      0      0      0    0.0             0          0   \n",
       "2   13.9      0   48.5      0      0      0    0.0             0          0   \n",
       "3   13.9      0   48.5      0      0      0    0.0             0          0   \n",
       "4   13.9      0   48.5      0      0      0    0.0             0          0   \n",
       "\n",
       "   EVBAP_Min  EVCCS  EVCM1  EVCTM  EVCCU  EVCSD  EVVCE  EVPSC_Latest  \\\n",
       "0          0      0    NaN    NaN    NaN    NaN    NaN           NaN   \n",
       "1          0      0    NaN    NaN    NaN    NaN    NaN           NaN   \n",
       "2          0      0    NaN    NaN    NaN    NaN    NaN           NaN   \n",
       "3          0      0    NaN    NaN    NaN    NaN    NaN           NaN   \n",
       "4          0      0    NaN    NaN    NaN    NaN    NaN           NaN   \n",
       "\n",
       "   EVPSC_Max  EVPSC_Min  EVVOU  EVCOU  EVCPV  EVVCD  EVCCD  EVCSC  EVEST  \\\n",
       "0        NaN        NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "1        NaN        NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "2        NaN        NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "3        NaN        NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "4        NaN        NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "\n",
       "   EVCHS  EVR10  EVRMN  EVHVS  EVV12  EVPWA_MAX  EVPWA_MIN  EVMCV_MAX  \\\n",
       "0    NaN    NaN    NaN     10   13.8       0.45        0.0       4.03   \n",
       "1    NaN    NaN    NaN     10   13.8       0.45        0.0       4.03   \n",
       "2    NaN    NaN    NaN     10   13.8       0.45        0.0       4.03   \n",
       "3    NaN    NaN    NaN     10   13.8       0.45        0.0       4.03   \n",
       "4    NaN    NaN    NaN     10   13.8       0.45        0.0       4.03   \n",
       "\n",
       "   EVMCV_MIN  EVSMA_MAX  EVSMA_MIN  EVSMI_MAX  EVSMI_MIN  EVSOH  EVBMA_Latest  \\\n",
       "0       4.03       92.9       92.9       92.6       92.6    100          15.5   \n",
       "1       4.03       92.9       92.9       92.6       92.6    100          15.5   \n",
       "2       4.03       92.9       92.9       92.6       92.6    100          15.5   \n",
       "3       4.03       92.9       92.9       92.6       92.6    100          15.5   \n",
       "4       4.03       92.9       92.9       92.6       92.6    100          15.5   \n",
       "\n",
       "   EVBMA_Max  EVBMA_Min  EVBMI_Latest  EVBMI_Max  EVBMI_Min  EVBOA_AVG  \\\n",
       "0       15.5       15.5          14.0       14.0       14.0       0.25   \n",
       "1       15.5       15.5          14.0       14.0       14.0       0.25   \n",
       "2       15.5       15.5          14.0       14.0       14.0       0.25   \n",
       "3       15.5       15.5          14.0       14.0       14.0       0.25   \n",
       "4       15.5       15.5          14.0       14.0       14.0       0.25   \n",
       "\n",
       "   EVBOA_MAX  EVBOA_MIN  EVBOV_AVG  EVBOV_MAX  EVBOV_MIN  EVIRP  EVIRN  \\\n",
       "0     -512.0     -328.0      267.0      186.0    16198.0   1830   1830   \n",
       "1     -512.0     -328.0      267.0      186.0    16198.0   1830   1830   \n",
       "2     -512.0     -328.0      267.0      186.0    16198.0   1830   1830   \n",
       "3     -512.0     -328.0      267.0      186.0    16198.0   1830   1830   \n",
       "4     -512.0     -328.0      267.0      186.0    16198.0   1830   1830   \n",
       "\n",
       "   EVSOMA  EVSOMI  EVIGM_Latest  EVIGM_Max  EVIGM_Min  EVCOM_Latest  \\\n",
       "0    99.9    99.9            11        -50        -38            11   \n",
       "1    99.9    99.9            11        -50        -38            11   \n",
       "2    99.9    99.9            11        -50        -38            11   \n",
       "3    99.9    99.9            11        -50        -38            11   \n",
       "4    99.9    99.9            11        -50        -38            11   \n",
       "\n",
       "   EVCOM_Max  EVCOM_Min  EVICO_Latest  EVICO_Max  EVICO_Min  EVIRT_Latest  \\\n",
       "0        166        203            11        -50        -41            11   \n",
       "1        166        203            11        -50        -41            11   \n",
       "2        166        203            11        -50        -41            11   \n",
       "3        166        203            11        -50        -41            11   \n",
       "4        166        203            11        -50        -41            11   \n",
       "\n",
       "   EVIRT_Max  EVIRT_Min  EVIDC  EVMGT  EVMGS  EVMGF  EVMGR  EVIND  EVICM  \\\n",
       "0        172        213  512.0   -0.2      0   -0.2   -0.2    270      3   \n",
       "1        172        213  512.0   -0.2      0   -0.2   -0.2    270      3   \n",
       "2        172        213  512.0   -0.2      0   -0.2   -0.2    270      3   \n",
       "3        172        213  512.0   -0.2      0   -0.2   -0.2    270      3   \n",
       "4        172        213  512.0   -0.2      0   -0.2   -0.2    270      3   \n",
       "\n",
       "   EVCPW  EVCPF_Latest  EVCPF_Max  EVCPF_Min  EVCI1_Latest  EVCI1_Max  \\\n",
       "0    NaN           NaN        NaN        NaN           NaN        NaN   \n",
       "1    NaN           NaN        NaN        NaN           NaN        NaN   \n",
       "2    NaN           NaN        NaN        NaN           NaN        NaN   \n",
       "3    NaN           NaN        NaN        NaN           NaN        NaN   \n",
       "4    NaN           NaN        NaN        NaN           NaN        NaN   \n",
       "\n",
       "   EVCI1_Min  EVCI2_Latest  EVCI2_Max  EVCI2_Min  EVCBD_Latest  EVCBD_Max  \\\n",
       "0        NaN           NaN        NaN        NaN           NaN        NaN   \n",
       "1        NaN           NaN        NaN        NaN           NaN        NaN   \n",
       "2        NaN           NaN        NaN        NaN           NaN        NaN   \n",
       "3        NaN           NaN        NaN        NaN           NaN        NaN   \n",
       "4        NaN           NaN        NaN        NaN           NaN        NaN   \n",
       "\n",
       "   EVCBD_Min  EVCRP  EVACV_AVG  EVACV_Max  EVACV_Min  EVCDO_AVG  EVCDO_Max  \\\n",
       "0        NaN      1        NaN        NaN        NaN        NaN        NaN   \n",
       "1        NaN      1        NaN        NaN        NaN        NaN        NaN   \n",
       "2        NaN      1        NaN        NaN        NaN        NaN        NaN   \n",
       "3        NaN      1        NaN        NaN        NaN        NaN        NaN   \n",
       "4        NaN      1        NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "   EVCDO_Min  EVCCO_AVG  EVCCO_Max  EVCCO_Min  EVSDT  EVCHC  EVCHT_AVG  \\\n",
       "0        NaN        NaN        NaN        NaN    NaN    NaN        NaN   \n",
       "1        NaN        NaN        NaN        NaN    NaN    NaN        NaN   \n",
       "2        NaN        NaN        NaN        NaN    NaN    NaN        NaN   \n",
       "3        NaN        NaN        NaN        NaN    NaN    NaN        NaN   \n",
       "4        NaN        NaN        NaN        NaN    NaN    NaN        NaN   \n",
       "\n",
       "   EVCHT_Max  EVCHT_Min  EVCHE  EVDI1  EVDI2  EVDIT  EVOII  EVDOA  EVDOV  \\\n",
       "0        NaN        NaN    NaN     11     11     11      0     21   13.9   \n",
       "1        NaN        NaN    NaN     11     11     11      0     21   13.9   \n",
       "2        NaN        NaN    NaN     11     11     11      0     21   13.9   \n",
       "3        NaN        NaN    NaN     11     11     11      0     21   13.9   \n",
       "4        NaN        NaN    NaN     11     11     11      0     21   13.9   \n",
       "\n",
       "   EVDSE  EVACS  EVPSS  EVMSC  EVRER  EVDRV  EVMTR  EVODO  EVOAS  EVHTR  \\\n",
       "0      0 -0.125 -279.0    0.7      0 -65532      0   4067   11.5      1   \n",
       "1      0 -0.125 -279.0    0.7      0 -65532      0   4067   11.5      1   \n",
       "2      0 -0.125 -279.0    0.7      0 -65532      0   4067   11.5      1   \n",
       "3      0 -0.125 -279.0    0.7      0 -65532      0   4067   11.5      1   \n",
       "4      0 -0.125 -279.0    0.7      0 -65532      0   4067   11.5      1   \n",
       "\n",
       "   EVACE  EVTRE  EVCLC  EVBFN  EVIST  EVHTP_AVG  EVHTP_Max  EVHTP_Min  EVVBT  \\\n",
       "0   10.7      4     65      1      0       4000       4000       4000    137   \n",
       "1   10.7      4     65      1      0       4000       4000       4000    137   \n",
       "2   10.7      4     65      1      0       4000       4000       4000    137   \n",
       "3   10.7      4     65      1      0       4000       4000       4000    137   \n",
       "4   10.7      4     65      1      0       4000       4000       4000    137   \n",
       "\n",
       "   EVGSM  EVACO_X  EVACO_Y  EVACO_Z  Unnamed: 164  \n",
       "0      0   -733.0    731.0      6.0           NaN  \n",
       "1      0   -733.0    731.0      6.0           NaN  \n",
       "2      0   -733.0    731.0      6.0           NaN  \n",
       "3      0   -733.0    731.0      6.0           NaN  \n",
       "4      0   -733.0    731.0      6.0           NaN  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_2 = data_preprocessor(data_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>EVBAP_Min</th>\n",
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       "      <th>EVVOU</th>\n",
       "      <th>EVCOU</th>\n",
       "      <th>EVCPV</th>\n",
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       "      <th>EVCSC</th>\n",
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       "      <th>EVCHS</th>\n",
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       "      <th>EVRMN</th>\n",
       "      <th>EVHVS</th>\n",
       "      <th>EVV12</th>\n",
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       "      <th>EVMCV_MIN</th>\n",
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       "      <th>EVSMA_MIN</th>\n",
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       "      <th>EVBMI_Max</th>\n",
       "      <th>EVBMI_Min</th>\n",
       "      <th>EVBOA_AVG</th>\n",
       "      <th>EVBOA_MAX</th>\n",
       "      <th>EVBOA_MIN</th>\n",
       "      <th>EVBOV_AVG</th>\n",
       "      <th>EVBOV_MAX</th>\n",
       "      <th>EVBOV_MIN</th>\n",
       "      <th>EVIRP</th>\n",
       "      <th>EVIRN</th>\n",
       "      <th>EVSOMA</th>\n",
       "      <th>EVSOMI</th>\n",
       "      <th>EVIGM_Latest</th>\n",
       "      <th>EVIGM_Max</th>\n",
       "      <th>EVIGM_Min</th>\n",
       "      <th>EVCOM_Latest</th>\n",
       "      <th>EVCOM_Max</th>\n",
       "      <th>EVCOM_Min</th>\n",
       "      <th>EVICO_Latest</th>\n",
       "      <th>EVICO_Max</th>\n",
       "      <th>EVICO_Min</th>\n",
       "      <th>EVIRT_Latest</th>\n",
       "      <th>EVIRT_Max</th>\n",
       "      <th>EVIRT_Min</th>\n",
       "      <th>EVIDC</th>\n",
       "      <th>EVMGT</th>\n",
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       "      <th>EVMGR</th>\n",
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       "      <th>EVCPF_Min</th>\n",
       "      <th>EVCI1_Latest</th>\n",
       "      <th>EVCI1_Max</th>\n",
       "      <th>EVCI1_Min</th>\n",
       "      <th>EVCI2_Latest</th>\n",
       "      <th>EVCI2_Max</th>\n",
       "      <th>EVCI2_Min</th>\n",
       "      <th>EVCBD_Latest</th>\n",
       "      <th>EVCBD_Max</th>\n",
       "      <th>EVCBD_Min</th>\n",
       "      <th>EVCRP</th>\n",
       "      <th>EVACV_AVG</th>\n",
       "      <th>EVACV_Max</th>\n",
       "      <th>EVACV_Min</th>\n",
       "      <th>EVCDO_AVG</th>\n",
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       "      <th>EVCCO_AVG</th>\n",
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       "      <th>EVCCO_Min</th>\n",
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       "      <th>EVCHT_Min</th>\n",
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       "      <th>EVRER</th>\n",
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       "      <th>EVODO</th>\n",
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       "      <th>EVACE</th>\n",
       "      <th>EVTRE</th>\n",
       "      <th>EVCLC</th>\n",
       "      <th>EVBFN</th>\n",
       "      <th>EVIST</th>\n",
       "      <th>EVHTP_AVG</th>\n",
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       "      <th>EVVBT</th>\n",
       "      <th>EVGSM</th>\n",
       "      <th>EVACO_X</th>\n",
       "      <th>EVACO_Y</th>\n",
       "      <th>EVACO_Z</th>\n",
       "      <th>Unnamed: 164</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1546918001380</td>\n",
       "      <td>77.073615</td>\n",
       "      <td>28.496959</td>\n",
       "      <td>0.657325</td>\n",
       "      <td>714359872</td>\n",
       "      <td>5</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>7</td>\n",
       "      <td>352891066262722</td>\n",
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       "      <td>M1_POCEV.0</td>\n",
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       "      <td>87</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
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       "      <td>267.0</td>\n",
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       "      <td>253.0</td>\n",
       "      <td>1830</td>\n",
       "      <td>1830</td>\n",
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       "      <td>172</td>\n",
       "      <td>213</td>\n",
       "      <td>512.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>13.9</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.125</td>\n",
       "      <td>-279.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0</td>\n",
       "      <td>-65532</td>\n",
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       "      <td>4067</td>\n",
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       "      <td>1</td>\n",
       "      <td>10.7</td>\n",
       "      <td>4</td>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4000</td>\n",
       "      <td>4000</td>\n",
       "      <td>4000</td>\n",
       "      <td>137</td>\n",
       "      <td>0</td>\n",
       "      <td>-733.0</td>\n",
       "      <td>731.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DEFREG:352891066262722</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>77.07351</td>\n",
       "      <td>28.49696</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100</td>\n",
       "      <td>1546918001480</td>\n",
       "      <td>77.073615</td>\n",
       "      <td>28.496959</td>\n",
       "      <td>0.657325</td>\n",
       "      <td>714359872</td>\n",
       "      <td>5</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
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       "      <td>92.9</td>\n",
       "      <td>92.6</td>\n",
       "      <td>92.6</td>\n",
       "      <td>100</td>\n",
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       "      <td>15.5</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>13.9</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.125</td>\n",
       "      <td>-279.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0</td>\n",
       "      <td>-65532</td>\n",
       "      <td>0</td>\n",
       "      <td>4067</td>\n",
       "      <td>11.5</td>\n",
       "      <td>1</td>\n",
       "      <td>10.7</td>\n",
       "      <td>4</td>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4000</td>\n",
       "      <td>4000</td>\n",
       "      <td>4000</td>\n",
       "      <td>137</td>\n",
       "      <td>0</td>\n",
       "      <td>-733.0</td>\n",
       "      <td>731.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DEFREG:352891066262722</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77.07351</td>\n",
       "      <td>28.49696</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200</td>\n",
       "      <td>1546918001580</td>\n",
       "      <td>77.073615</td>\n",
       "      <td>28.496959</td>\n",
       "      <td>0.657325</td>\n",
       "      <td>714359872</td>\n",
       "      <td>5</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>7</td>\n",
       "      <td>352891066262722</td>\n",
       "      <td>Trip not started</td>\n",
       "      <td>P</td>\n",
       "      <td>M1_POCEV.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>877</td>\n",
       "      <td>0.0</td>\n",
       "      <td>87</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>13.9</td>\n",
       "      <td>0</td>\n",
       "      <td>48.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>13.8</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.03</td>\n",
       "      <td>4.03</td>\n",
       "      <td>92.9</td>\n",
       "      <td>92.9</td>\n",
       "      <td>92.6</td>\n",
       "      <td>92.6</td>\n",
       "      <td>100</td>\n",
       "      <td>15.5</td>\n",
       "      <td>15.5</td>\n",
       "      <td>15.5</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>12.5</td>\n",
       "      <td>10.0</td>\n",
       "      <td>267.0</td>\n",
       "      <td>252.5</td>\n",
       "      <td>253.0</td>\n",
       "      <td>1830</td>\n",
       "      <td>1830</td>\n",
       "      <td>99.9</td>\n",
       "      <td>99.9</td>\n",
       "      <td>11</td>\n",
       "      <td>-50</td>\n",
       "      <td>-38</td>\n",
       "      <td>11</td>\n",
       "      <td>166</td>\n",
       "      <td>203</td>\n",
       "      <td>11</td>\n",
       "      <td>-50</td>\n",
       "      <td>-41</td>\n",
       "      <td>11</td>\n",
       "      <td>172</td>\n",
       "      <td>213</td>\n",
       "      <td>512.0</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>270</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>13.9</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.125</td>\n",
       "      <td>-279.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0</td>\n",
       "      <td>-65532</td>\n",
       "      <td>0</td>\n",
       "      <td>4067</td>\n",
       "      <td>11.5</td>\n",
       "      <td>1</td>\n",
       "      <td>10.7</td>\n",
       "      <td>4</td>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4000</td>\n",
       "      <td>4000</td>\n",
       "      <td>4000</td>\n",
       "      <td>137</td>\n",
       "      <td>0</td>\n",
       "      <td>-733.0</td>\n",
       "      <td>731.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DEFREG:352891066262722</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77.07351</td>\n",
       "      <td>28.49696</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300</td>\n",
       "      <td>1546918001680</td>\n",
       "      <td>77.073615</td>\n",
       "      <td>28.496959</td>\n",
       "      <td>0.657325</td>\n",
       "      <td>714359872</td>\n",
       "      <td>5</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>7</td>\n",
       "      <td>352891066262722</td>\n",
       "      <td>Trip not started</td>\n",
       "      <td>P</td>\n",
       "      <td>M1_POCEV.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>877</td>\n",
       "      <td>0.0</td>\n",
       "      <td>87</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>13.9</td>\n",
       "      <td>0</td>\n",
       "      <td>48.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>13.8</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.03</td>\n",
       "      <td>4.03</td>\n",
       "      <td>92.9</td>\n",
       "      <td>92.9</td>\n",
       "      <td>92.6</td>\n",
       "      <td>92.6</td>\n",
       "      <td>100</td>\n",
       "      <td>15.5</td>\n",
       "      <td>15.5</td>\n",
       "      <td>15.5</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>12.5</td>\n",
       "      <td>10.0</td>\n",
       "      <td>267.0</td>\n",
       "      <td>252.5</td>\n",
       "      <td>253.0</td>\n",
       "      <td>1830</td>\n",
       "      <td>1830</td>\n",
       "      <td>99.9</td>\n",
       "      <td>99.9</td>\n",
       "      <td>11</td>\n",
       "      <td>-50</td>\n",
       "      <td>-38</td>\n",
       "      <td>11</td>\n",
       "      <td>166</td>\n",
       "      <td>203</td>\n",
       "      <td>11</td>\n",
       "      <td>-50</td>\n",
       "      <td>-41</td>\n",
       "      <td>11</td>\n",
       "      <td>172</td>\n",
       "      <td>213</td>\n",
       "      <td>512.0</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>270</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>13.9</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.125</td>\n",
       "      <td>-279.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0</td>\n",
       "      <td>-65532</td>\n",
       "      <td>0</td>\n",
       "      <td>4067</td>\n",
       "      <td>11.5</td>\n",
       "      <td>1</td>\n",
       "      <td>10.7</td>\n",
       "      <td>4</td>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4000</td>\n",
       "      <td>4000</td>\n",
       "      <td>4000</td>\n",
       "      <td>137</td>\n",
       "      <td>0</td>\n",
       "      <td>-733.0</td>\n",
       "      <td>731.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DEFREG:352891066262722</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77.07351</td>\n",
       "      <td>28.49696</td>\n",
       "      <td>NaN</td>\n",
       "      <td>400</td>\n",
       "      <td>1546918001780</td>\n",
       "      <td>77.073615</td>\n",
       "      <td>28.496959</td>\n",
       "      <td>0.657325</td>\n",
       "      <td>714359872</td>\n",
       "      <td>5</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>7</td>\n",
       "      <td>352891066262722</td>\n",
       "      <td>Trip not started</td>\n",
       "      <td>P</td>\n",
       "      <td>M1_POCEV.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>877</td>\n",
       "      <td>0.0</td>\n",
       "      <td>87</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>13.9</td>\n",
       "      <td>0</td>\n",
       "      <td>48.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>13.8</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.03</td>\n",
       "      <td>4.03</td>\n",
       "      <td>92.9</td>\n",
       "      <td>92.9</td>\n",
       "      <td>92.6</td>\n",
       "      <td>92.6</td>\n",
       "      <td>100</td>\n",
       "      <td>15.5</td>\n",
       "      <td>15.5</td>\n",
       "      <td>15.5</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>12.5</td>\n",
       "      <td>10.0</td>\n",
       "      <td>267.0</td>\n",
       "      <td>252.5</td>\n",
       "      <td>253.0</td>\n",
       "      <td>1830</td>\n",
       "      <td>1830</td>\n",
       "      <td>99.9</td>\n",
       "      <td>99.9</td>\n",
       "      <td>11</td>\n",
       "      <td>-50</td>\n",
       "      <td>-38</td>\n",
       "      <td>11</td>\n",
       "      <td>166</td>\n",
       "      <td>203</td>\n",
       "      <td>11</td>\n",
       "      <td>-50</td>\n",
       "      <td>-41</td>\n",
       "      <td>11</td>\n",
       "      <td>172</td>\n",
       "      <td>213</td>\n",
       "      <td>512.0</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>270</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>13.9</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.125</td>\n",
       "      <td>-279.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0</td>\n",
       "      <td>-65532</td>\n",
       "      <td>0</td>\n",
       "      <td>4067</td>\n",
       "      <td>11.5</td>\n",
       "      <td>1</td>\n",
       "      <td>10.7</td>\n",
       "      <td>4</td>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4000</td>\n",
       "      <td>4000</td>\n",
       "      <td>4000</td>\n",
       "      <td>137</td>\n",
       "      <td>0</td>\n",
       "      <td>-733.0</td>\n",
       "      <td>731.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       mo  tp  dr        ln        lt  hd   sp             tm  \\\n",
       "0  DEFREG:352891066262722   1 NaN  77.07351  28.49696 NaN    0  1546918001380   \n",
       "1  DEFREG:352891066262722   1 NaN  77.07351  28.49696 NaN  100  1546918001480   \n",
       "2  DEFREG:352891066262722   1 NaN  77.07351  28.49696 NaN  200  1546918001580   \n",
       "3  DEFREG:352891066262722   1 NaN  77.07351  28.49696 NaN  300  1546918001680   \n",
       "4  DEFREG:352891066262722   1 NaN  77.07351  28.49696 NaN  400  1546918001780   \n",
       "\n",
       "          mn         mt        mh         ml  mr  SpeedLimit  hdop  numsat  \\\n",
       "0  77.073615  28.496959  0.657325  714359872   5        30.0   1.1       7   \n",
       "1  77.073615  28.496959  0.657325  714359872   5        30.0   1.1       7   \n",
       "2  77.073615  28.496959  0.657325  714359872   5        30.0   1.1       7   \n",
       "3  77.073615  28.496959  0.657325  714359872   5        30.0   1.1       7   \n",
       "4  77.073615  28.496959  0.657325  714359872   5        30.0   1.1       7   \n",
       "\n",
       "              IMEI           trip_id EVTmg       EVVer  EVCfg  EVIGS  EVIGC  \\\n",
       "0  352891066262722  Trip not started     P  M1_POCEV.0    NaN      1    877   \n",
       "1  352891066262722  Trip not started     P  M1_POCEV.0    NaN      1    877   \n",
       "2  352891066262722  Trip not started     P  M1_POCEV.0    NaN      1    877   \n",
       "3  352891066262722  Trip not started     P  M1_POCEV.0    NaN      1    877   \n",
       "4  352891066262722  Trip not started     P  M1_POCEV.0    NaN      1    877   \n",
       "\n",
       "   EVVSP  EVDRG  EVGPO  EVBRP  EVCFN  EVICR  EVTRQ  EVCST  EVDDC  EVBCA  \\\n",
       "0    0.0     87     10      0      0      1    0.0      1   13.9      0   \n",
       "1    0.0     87     10      0      0      1    0.0      1   13.9      0   \n",
       "2    0.0     87     10      0      0      1    0.0      1   13.9      0   \n",
       "3    0.0     87     10      0      0      1    0.0      1   13.9      0   \n",
       "4    0.0     87     10      0      0      1    0.0      1   13.9      0   \n",
       "\n",
       "   EVBBV  EVDR1  EVDR2  EVRGT  EVACP  EVBAP_Latest  EVBAP_Max  EVBAP_Min  \\\n",
       "0   48.5      0      0      0    0.0             0          0          0   \n",
       "1   48.5      0      0      0    0.0             0          0          0   \n",
       "2   48.5      0      0      0    0.0             0          0          0   \n",
       "3   48.5      0      0      0    0.0             0          0          0   \n",
       "4   48.5      0      0      0    0.0             0          0          0   \n",
       "\n",
       "   EVCCS  EVCM1  EVCTM  EVCCU  EVCSD  EVVCE  EVPSC_Latest  EVPSC_Max  \\\n",
       "0      0    NaN    NaN    NaN    NaN    NaN           NaN        NaN   \n",
       "1      0    NaN    NaN    NaN    NaN    NaN           NaN        NaN   \n",
       "2      0    NaN    NaN    NaN    NaN    NaN           NaN        NaN   \n",
       "3      0    NaN    NaN    NaN    NaN    NaN           NaN        NaN   \n",
       "4      0    NaN    NaN    NaN    NaN    NaN           NaN        NaN   \n",
       "\n",
       "   EVPSC_Min  EVVOU  EVCOU  EVCPV  EVVCD  EVCCD  EVCSC  EVEST  EVCHS  EVR10  \\\n",
       "0        NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "1        NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "2        NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "3        NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "4        NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "\n",
       "   EVRMN  EVHVS  EVV12  EVPWA_MAX  EVPWA_MIN  EVMCV_MAX  EVMCV_MIN  EVSMA_MAX  \\\n",
       "0    NaN     10   13.8       0.45        0.0       4.03       4.03       92.9   \n",
       "1    NaN     10   13.8       0.45        0.0       4.03       4.03       92.9   \n",
       "2    NaN     10   13.8       0.45        0.0       4.03       4.03       92.9   \n",
       "3    NaN     10   13.8       0.45        0.0       4.03       4.03       92.9   \n",
       "4    NaN     10   13.8       0.45        0.0       4.03       4.03       92.9   \n",
       "\n",
       "   EVSMA_MIN  EVSMI_MAX  EVSMI_MIN  EVSOH  EVBMA_Latest  EVBMA_Max  EVBMA_Min  \\\n",
       "0       92.9       92.6       92.6    100          15.5       15.5       15.5   \n",
       "1       92.9       92.6       92.6    100          15.5       15.5       15.5   \n",
       "2       92.9       92.6       92.6    100          15.5       15.5       15.5   \n",
       "3       92.9       92.6       92.6    100          15.5       15.5       15.5   \n",
       "4       92.9       92.6       92.6    100          15.5       15.5       15.5   \n",
       "\n",
       "   EVBMI_Latest  EVBMI_Max  EVBMI_Min  EVBOA_AVG  EVBOA_MAX  EVBOA_MIN  \\\n",
       "0          14.0       14.0       14.0       0.25       12.5       10.0   \n",
       "1          14.0       14.0       14.0       0.25       12.5       10.0   \n",
       "2          14.0       14.0       14.0       0.25       12.5       10.0   \n",
       "3          14.0       14.0       14.0       0.25       12.5       10.0   \n",
       "4          14.0       14.0       14.0       0.25       12.5       10.0   \n",
       "\n",
       "   EVBOV_AVG  EVBOV_MAX  EVBOV_MIN  EVIRP  EVIRN  EVSOMA  EVSOMI  \\\n",
       "0      267.0      252.5      253.0   1830   1830    99.9    99.9   \n",
       "1      267.0      252.5      253.0   1830   1830    99.9    99.9   \n",
       "2      267.0      252.5      253.0   1830   1830    99.9    99.9   \n",
       "3      267.0      252.5      253.0   1830   1830    99.9    99.9   \n",
       "4      267.0      252.5      253.0   1830   1830    99.9    99.9   \n",
       "\n",
       "   EVIGM_Latest  EVIGM_Max  EVIGM_Min  EVCOM_Latest  EVCOM_Max  EVCOM_Min  \\\n",
       "0            11        -50        -38            11        166        203   \n",
       "1            11        -50        -38            11        166        203   \n",
       "2            11        -50        -38            11        166        203   \n",
       "3            11        -50        -38            11        166        203   \n",
       "4            11        -50        -38            11        166        203   \n",
       "\n",
       "   EVICO_Latest  EVICO_Max  EVICO_Min  EVIRT_Latest  EVIRT_Max  EVIRT_Min  \\\n",
       "0            11        -50        -41            11        172        213   \n",
       "1            11        -50        -41            11        172        213   \n",
       "2            11        -50        -41            11        172        213   \n",
       "3            11        -50        -41            11        172        213   \n",
       "4            11        -50        -41            11        172        213   \n",
       "\n",
       "   EVIDC  EVMGT  EVMGS  EVMGF  EVMGR  EVIND  EVICM  EVCPW  EVCPF_Latest  \\\n",
       "0  512.0   -0.2      0   -0.2   -0.2    270      3    NaN           NaN   \n",
       "1  512.0   -0.2      0   -0.2   -0.2    270      3    NaN           NaN   \n",
       "2  512.0   -0.2      0   -0.2   -0.2    270      3    NaN           NaN   \n",
       "3  512.0   -0.2      0   -0.2   -0.2    270      3    NaN           NaN   \n",
       "4  512.0   -0.2      0   -0.2   -0.2    270      3    NaN           NaN   \n",
       "\n",
       "   EVCPF_Max  EVCPF_Min  EVCI1_Latest  EVCI1_Max  EVCI1_Min  EVCI2_Latest  \\\n",
       "0        NaN        NaN           NaN        NaN        NaN           NaN   \n",
       "1        NaN        NaN           NaN        NaN        NaN           NaN   \n",
       "2        NaN        NaN           NaN        NaN        NaN           NaN   \n",
       "3        NaN        NaN           NaN        NaN        NaN           NaN   \n",
       "4        NaN        NaN           NaN        NaN        NaN           NaN   \n",
       "\n",
       "   EVCI2_Max  EVCI2_Min  EVCBD_Latest  EVCBD_Max  EVCBD_Min  EVCRP  EVACV_AVG  \\\n",
       "0        NaN        NaN           NaN        NaN        NaN      1        NaN   \n",
       "1        NaN        NaN           NaN        NaN        NaN      1        NaN   \n",
       "2        NaN        NaN           NaN        NaN        NaN      1        NaN   \n",
       "3        NaN        NaN           NaN        NaN        NaN      1        NaN   \n",
       "4        NaN        NaN           NaN        NaN        NaN      1        NaN   \n",
       "\n",
       "   EVACV_Max  EVACV_Min  EVCDO_AVG  EVCDO_Max  EVCDO_Min  EVCCO_AVG  \\\n",
       "0        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "1        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "3        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "4        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "   EVCCO_Max  EVCCO_Min  EVSDT  EVCHC  EVCHT_AVG  EVCHT_Max  EVCHT_Min  EVCHE  \\\n",
       "0        NaN        NaN    NaN    NaN        NaN        NaN        NaN    NaN   \n",
       "1        NaN        NaN    NaN    NaN        NaN        NaN        NaN    NaN   \n",
       "2        NaN        NaN    NaN    NaN        NaN        NaN        NaN    NaN   \n",
       "3        NaN        NaN    NaN    NaN        NaN        NaN        NaN    NaN   \n",
       "4        NaN        NaN    NaN    NaN        NaN        NaN        NaN    NaN   \n",
       "\n",
       "   EVDI1  EVDI2  EVDIT  EVOII  EVDOA  EVDOV  EVDSE  EVACS  EVPSS  EVMSC  \\\n",
       "0     11     11     11      0     21   13.9      0 -0.125 -279.0    0.7   \n",
       "1     11     11     11      0     21   13.9      0 -0.125 -279.0    0.7   \n",
       "2     11     11     11      0     21   13.9      0 -0.125 -279.0    0.7   \n",
       "3     11     11     11      0     21   13.9      0 -0.125 -279.0    0.7   \n",
       "4     11     11     11      0     21   13.9      0 -0.125 -279.0    0.7   \n",
       "\n",
       "   EVRER  EVDRV  EVMTR  EVODO  EVOAS  EVHTR  EVACE  EVTRE  EVCLC  EVBFN  \\\n",
       "0      0 -65532      0   4067   11.5      1   10.7      4     65      1   \n",
       "1      0 -65532      0   4067   11.5      1   10.7      4     65      1   \n",
       "2      0 -65532      0   4067   11.5      1   10.7      4     65      1   \n",
       "3      0 -65532      0   4067   11.5      1   10.7      4     65      1   \n",
       "4      0 -65532      0   4067   11.5      1   10.7      4     65      1   \n",
       "\n",
       "   EVIST  EVHTP_AVG  EVHTP_Max  EVHTP_Min  EVVBT  EVGSM  EVACO_X  EVACO_Y  \\\n",
       "0      0       4000       4000       4000    137      0   -733.0    731.0   \n",
       "1      0       4000       4000       4000    137      0   -733.0    731.0   \n",
       "2      0       4000       4000       4000    137      0   -733.0    731.0   \n",
       "3      0       4000       4000       4000    137      0   -733.0    731.0   \n",
       "4      0       4000       4000       4000    137      0   -733.0    731.0   \n",
       "\n",
       "   EVACO_Z  Unnamed: 164  \n",
       "0      6.0           NaN  \n",
       "1      6.0           NaN  \n",
       "2      6.0           NaN  \n",
       "3      6.0           NaN  \n",
       "4      6.0           NaN  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def trip_data(data, trip_number, col_1, col_2):\n",
    "    \"\"\"\n",
    "    This function returns a particular trip data, with EVSMA_Smoothened and Delta EVSMA. along with the column for \n",
    "    efficient comparision.\n",
    "    \"\"\"\n",
    "    trip_df = data[data[\"tp\"] == trip_number]\n",
    "    trip_df = trip_df[\"EVSMA_MAX\"].to_frame()\n",
    "    span = 600\n",
    "    alpha = 2/(1 + span)\n",
    "    trip_df[\"EVSMA_EWMA\"] = np.nan\n",
    "    trip_df[\"EVSMA_EWMA\"].iloc[0] = trip_df[\"EVSMA_MAX\"].iloc[0]\n",
    "    \n",
    "    for i in range(1, len(trip_df)):\n",
    "        temp = (trip_df[\"EVSMA_MAX\"].iloc[i]*alpha) + (trip_df[\"EVSMA_EWMA\"].iloc[i-1]*(1-alpha))\n",
    "        trip_df[\"EVSMA_EWMA\"].iloc[i] = temp\n",
    "    \n",
    "    trip_df[\"EVSMA_Shift\"] = trip_df[\"EVSMA_EWMA\"].shift(periods = 1)\n",
    "    trip_df[\"EVSMA_Shift\"].iloc[0] = trip_df[\"EVSMA_EWMA\"].iloc[0]\n",
    "    trip_df[\"EVSMA_EWMA\"] = abs(trip_df[\"EVSMA_EWMA\"] - trip_df[\"EVSMA_Shift\"])\n",
    "    \n",
    "    trip_df[col_1] = data[data[\"tp\"] == trip_number][col_1]\n",
    "    trip_df[col_2] = data[data[\"tp\"] == trip_number][col_2]\n",
    "    \n",
    "    df = trip_df[[col_1, col_2, \"EVSMA_MAX\", \"EVSMA_EWMA\"]][600:]\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def relative_corr(data, col_1, col_2):\n",
    "    \"\"\"\n",
    "    This function returns the Correlation dataframe of target variable with two attributes\n",
    "    \"\"\"\n",
    "    corr_1 = []\n",
    "    corr_2 = []\n",
    "    corr_3 = []\n",
    "    length = []\n",
    "    for i in range(0, len(list(data[\"tp\"].unique()))):\n",
    "        trip_t = data[data[\"tp\"] == i+1]\n",
    "        trip_t = trip_t[\"EVSMA_MAX\"].to_frame()\n",
    "        span = 600\n",
    "        alpha = 2/(1 + span)\n",
    "        trip_t['EVSMA_EWMA'] = np.nan\n",
    "        trip_t['EVSMA_EWMA'].iloc[0] = trip_t[\"EVSMA_MAX\"].iloc[0]\n",
    "        \n",
    "        for j in range(1, len(trip_t)):\n",
    "            temp = (trip_t[\"EVSMA_MAX\"].iloc[j]*alpha) + (trip_t[\"EVSMA_EWMA\"].iloc[j-1]*(1-alpha))\n",
    "            trip_t[\"EVSMA_EWMA\"].iloc[j] = temp\n",
    "            \n",
    "        trip_t[\"EVSMA_Shift\"] = trip_t[\"EVSMA_EWMA\"].shift(periods = 1)\n",
    "        trip_t[\"EVSMA_Shift\"].iloc[0] = trip_t[\"EVSMA_EWMA\"].iloc[0]\n",
    "        trip_t[\"EVSMA_delta\"] = abs(trip_t[\"EVSMA_EWMA\"] - trip_t[\"EVSMA_Shift\"])\n",
    "        \n",
    "        trip_t[col_1] = data[data[\"tp\"] == i+1][col_1]\n",
    "        trip_t[col_2] = data[data[\"tp\"] == i+1][col_2]\n",
    "        \n",
    "        temp_data = trip_t[[col_1, col_2, \"EVSMA_MAX\", \"EVSMA_EWMA\"]][600:]\n",
    "        c1 = temp_data[col_1].corr(temp_data[\"EVSMA_MAX\"])\n",
    "        c2 = temp_data[col_2].corr(temp_data[\"EVSMA_MAX\"])\n",
    "        c3 = temp_data[col_1].corr(temp_data[col_2])\n",
    "        l = len(temp_data)\n",
    "        #v = temp_data.IMEI.iloc[0]\n",
    "        #print(c1, c2, c3)\n",
    "        \n",
    "        corr_1.append(round(c1, 2))\n",
    "        corr_2.append(round(c2, 2))\n",
    "        corr_3.append(round(c3, 2))\n",
    "        length.append(l)\n",
    "        \n",
    "        a = col_1 + \" & SMA_MAX\"\n",
    "        b = col_2 + \" & SMA_MAX\"\n",
    "        ab = col_1 + \" & \" + col_2\n",
    "    \n",
    "    SMA_corr = pd.DataFrame({\n",
    "        \"Length\": length,\n",
    "        a: corr_1,\n",
    "        b: corr_2,\n",
    "        ab: corr_3\n",
    "    })\n",
    "    \n",
    "    return SMA_corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def corr_scatterplot(data):\n",
    "    \"\"\"\n",
    "    this function plots the scatter plot of length of the trip vs correlation of attribute with EVSMA_MAX\n",
    "    \"\"\"\n",
    "    label_1 = data.columns[1]\n",
    "    label_2 = data.columns[2]\n",
    "    label_3 = data.columns[3]\n",
    "    sns.scatterplot(x = data[\"Length\"], \n",
    "                    y = data.iloc[:, 1], \n",
    "                    color = \"red\", \n",
    "                    label = label_1)\n",
    "    sns.scatterplot(x = data[\"Length\"], \n",
    "                    y = data.iloc[:, 2], \n",
    "                    color = \"black\", \n",
    "                    label = label_2)\n",
    "    sns.scatterplot(x = data[\"Length\"], \n",
    "                    y = data.iloc[:, 3], \n",
    "                    color = \"blue\", \n",
    "                    label = label_3)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def eval_pdf(rv, num = 4):\n",
    "    mean, std_dev = rv.mean(), rv.std()\n",
    "    xs = np.linspace(mean - (num*std_dev), mean+(num*std_dev), 100)\n",
    "    ys = rv.pdf(xs)\n",
    "    return xs, ys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cohen_effect_size(group1, group2):\n",
    "    diff = group1.mean() - group2.mean()\n",
    "    n1, n2 = len(group1), len(group1)\n",
    "    var1, var2 = group1.var(), group2.var()\n",
    "    pooled_var = ((n1*var1) + (n2*var2))/(n1 + n2)\n",
    "    d = diff/np.sqrt(pooled_var)\n",
    "    return d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def overlap_superiority(control, treatment, n = 1000):\n",
    "    \"\"\"\n",
    "    Estimates overlap and superiority based on the sample\n",
    "    control - rv object\n",
    "    treatment - rv object\n",
    "    n: samples\n",
    "    \"\"\"\n",
    "    control_samples = control.rvs()\n",
    "    treatment_samples = treatment.rvs()\n",
    "    threshold = (control.mean() + treatment.mean())/2\n",
    "    control_above = sum(control_samples > threshold)\n",
    "    treatment_below = sum(treatment_samples < threshold)\n",
    "    overlap = (control_above + treatment_below)/n\n",
    "    superiority = (treatment_samples > control_samples).mean()\n",
    "    return overlap, superiority"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_pdfs(cohen_d):\n",
    "    \"\"\"\n",
    "    plot PDFs for distributions that differ by some number of standard deviations\n",
    "    cohen_d: number of standard deviations between the mean\n",
    "    \"\"\"\n",
    "    control = sps.norm(0, 1)\n",
    "    treatment = sps.norm(cohen_d, 1)\n",
    "    xs, ys = eval_pdf(control)\n",
    "    plt.fill_between(xs, ys, label = \"control\", color = \"blue\", alpha = 0.7)\n",
    "    \n",
    "    xs, ys = eval_pdf(treatment)\n",
    "    plt.fill_between(xs, ys, label = \"treatment\", color = \"red\", alpha = 0.7)\n",
    "    \n",
    "    #o, s = overlap_superiority(control, treatment)\n",
    "    #plt.text(0, 0.05, \"overlap\" + str(o))\n",
    "    #plt.text(0, 0.15, \"superioty\" + str(s))\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_2[\"tp\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "44400"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_df = trip_data(data, 39, \"EVIND\", \"EVHTP_AVG\") \n",
    "len(temp_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EVIND</th>\n",
       "      <th>EVHTP_AVG</th>\n",
       "      <th>EVSMA_MAX</th>\n",
       "      <th>EVSMA_EWMA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>698121</th>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>62.7</td>\n",
       "      <td>0.000437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>708638</th>\n",
       "      <td>248</td>\n",
       "      <td>0</td>\n",
       "      <td>56.9</td>\n",
       "      <td>0.000931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>698060</th>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>62.7</td>\n",
       "      <td>0.000536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>718723</th>\n",
       "      <td>244</td>\n",
       "      <td>0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.000324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>701158</th>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>61.1</td>\n",
       "      <td>0.000660</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        EVIND  EVHTP_AVG  EVSMA_MAX  EVSMA_EWMA\n",
       "698121    250          0       62.7    0.000437\n",
       "708638    248          0       56.9    0.000931\n",
       "698060    250          0       62.7    0.000536\n",
       "718723    244          0       52.0    0.000324\n",
       "701158    250          0       61.1    0.000660"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x = \"EVIND\", y = \"EVSMA_MAX\", data = temp_df, kind = \"kde\", color = \"blue\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp_corr_data = relative_corr(data, \"EVIND\", \"EVHTP_AVG\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Length</th>\n",
       "      <th>EVIND &amp; SMA_MAX</th>\n",
       "      <th>EVHTP_AVG &amp; SMA_MAX</th>\n",
       "      <th>EVIND &amp; EVHTP_AVG</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>24600</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23400</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3000</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>23400</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>11400</td>\n",
       "      <td>0.76</td>\n",
       "      <td>-0.26</td>\n",
       "      <td>-0.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>19198</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>9600</td>\n",
       "      <td>0.07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1200</td>\n",
       "      <td>0.16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>4200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>65400</td>\n",
       "      <td>0.98</td>\n",
       "      <td>-0.32</td>\n",
       "      <td>-0.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>3000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>10800</td>\n",
       "      <td>-0.84</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>3000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.56</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>72000</td>\n",
       "      <td>0.98</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>4800</td>\n",
       "      <td>0.36</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>-0.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1200</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>12600</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>30600</td>\n",
       "      <td>0.87</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>30000</td>\n",
       "      <td>0.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>39000</td>\n",
       "      <td>0.96</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>25200</td>\n",
       "      <td>0.85</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>55200</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>3000</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>82200</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>61800</td>\n",
       "      <td>0.97</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>8400</td>\n",
       "      <td>0.83</td>\n",
       "      <td>-0.79</td>\n",
       "      <td>-0.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>34800</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>44400</td>\n",
       "      <td>0.96</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>27000</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>28200</td>\n",
       "      <td>0.93</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>-0.03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Length  EVIND & SMA_MAX  EVHTP_AVG & SMA_MAX  EVIND & EVHTP_AVG\n",
       "0        0              NaN                  NaN                NaN\n",
       "1    24600             0.93                 0.15               0.16\n",
       "2    23400             0.75                 0.16               0.14\n",
       "3        0              NaN                  NaN                NaN\n",
       "4        0              NaN                  NaN                NaN\n",
       "5        0              NaN                  NaN                NaN\n",
       "6     3000             0.08                 0.75               0.05\n",
       "7    23400             0.92                 0.38               0.35\n",
       "8    11400             0.76                -0.26              -0.32\n",
       "9    19198             0.41                 0.37               0.14\n",
       "10    9600             0.07                  NaN                NaN\n",
       "11       0              NaN                  NaN                NaN\n",
       "12    1200             0.16                  NaN                NaN\n",
       "13       0              NaN                  NaN                NaN\n",
       "14    4200              NaN                  NaN                NaN\n",
       "15   65400             0.98                -0.32              -0.33\n",
       "16       0              NaN                  NaN                NaN\n",
       "17       0              NaN                  NaN                NaN\n",
       "18    3000              NaN                  NaN                NaN\n",
       "19   10800            -0.84                 0.03               0.23\n",
       "20       0              NaN                  NaN                NaN\n",
       "21    3000              NaN                 0.56                NaN\n",
       "22   72000             0.98                -0.01              -0.00\n",
       "23    4800             0.36                -0.23              -0.21\n",
       "24    1200            -0.00                 0.00              -0.95\n",
       "25   12600             0.75                 0.32               0.22\n",
       "26   30600             0.87                  NaN                NaN\n",
       "27   30000             0.90                  NaN                NaN\n",
       "28   39000             0.96                  NaN                NaN\n",
       "29   25200             0.85                  NaN                NaN\n",
       "30   55200             0.99                 0.19               0.21\n",
       "31    3000            -0.01                 0.14               0.26\n",
       "32       0              NaN                  NaN                NaN\n",
       "33       0              NaN                  NaN                NaN\n",
       "34   82200             0.98                 0.44               0.43\n",
       "35   61800             0.97                  NaN                NaN\n",
       "36    8400             0.83                -0.79              -0.58\n",
       "37   34800             0.96                 0.10               0.13\n",
       "38   44400             0.96                -0.03              -0.01\n",
       "39       0              NaN                  NaN                NaN\n",
       "40   27000             0.96                 0.02               0.03\n",
       "41   28200             0.93                -0.07              -0.03"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_corr_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr_scatterplot(temp_corr_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.32622435463850247"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.EVIND.corr(data.EVSMA_MAX)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "effect_size = cohen_effect_size(b, a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9774690035717833\n"
     ]
    }
   ],
   "source": [
    "print(effect_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_pdfs(effect_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24600"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_df = trip_data(data, 2, \"EVHTR\", \"EVOAS\") \n",
    "len(temp_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(temp_df[\"EVHTR\"]*10, label = \"EVHTR\")\n",
    "plt.plot(temp_df[\"EVOAS\"], label = \"EVOAS\")\n",
    "plt.legend()\n",
    "plt.title(\"HTR vs OAS\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/fnyTK25ubg5nn/1k3Hbbxyu5nihN0x2PAe12U+zff2SnTyt2gD175nHw4NK4pyGGUPe92fsXz0Lrwa/jwEs/jPVTHl/b504qenbCRfdmOPfe+y08/OGnj3UOSRJjfT2sjdmw72Xfvl2msbI0CdFUijpNcs8JIYQFiSYhGkoaZyZxueeEEMKGRJMQTaUnmtBdG+88hBCVUUEEzkRT9vuQaBKiqcg9J8RU02p1cPToIQmnHmma4ujRQ2i1Ou5zqOSAEE2lsDRJNAkxjezduw8PPng/jhw5OLY5RFEUlGhrtTrYu3eff/wOzkUIMUH0Sw7IPSfENJIkLZx66j8Z6xymLbNR7jkhmkqUW5oUCC6EEBYkmoRoKrl7TpYmIYQwIdEkREMp3HOKaRJCCBOmmKaLLroICwsLiOMYSZLguuuuq3peQoiqKSxNEk1CCGHBHAj+J3/yJzj55JOrnIsQok7ykgOq0ySEECbknhOiqRSWJgWCCyGEBbOl6VWvehWiKMKVV16JK6+8cttjoyhLMxThkSSx7k2g1H1vkpkZAMDCXIJ5/SZGomcnXHRvwmXa7o1JNP3lX/4lTjvtNOzfvx+vfOUrccYZZ+ApT3nKlsenKaaqLsM0MW01M6aJuu/NrrUUswCWjixhWb+JkejZCRfdm3CZlHuzb98u03Em99xpp50GADjllFNwySWX4Itf/KJ/ZkKIMFBFcCGEoBgpmpaWlnDkyJHiz7fddhse+9jHVj4xIUS1qCK4EEJwjHTP7d+/H6997WsBAOvr63jhC1+ICy+8sPKJCSEqRhXBhRCCYqRoesQjHoH3vOc9dcxFCFEnqgguhBAUKjkgRENRRXAhhOCQaBKiqagiuBBCUEg0CdFUVBFcCCEoJJqEaCqqCC6EEBQSTUI0lDRKAACRLE1CCGFCokmIphJnokkxTUIIYUOiSYimoorgQghBIdEkRGPJK4JLNAkhhAWJJiEaShpF2R8U0ySEECYkmoRoOrI0CSGECYkmIZqOYpqEEMKERJMQDUcxTUIIYUOiSYimI0uTEEKYkGgSoumkCgQXQggLEk1CNJxIliYhhDAh0SRE01FMkxBCmJBoEqLpyNIkhBAmJJqEaDjKnhNCCBsSTUI0HVUEF0IIExJNQjSdtDvuGQghxEQg0SRE05GlSQghTEg0CdFwFNMkhBA2JJqEaDrKnhNCCBMSTUI0HVUEF0IIExJNQjScSIHgQghhQqJJiKajQHAhhDAh0SRE01FMkxBCmJBoEqLpKHtOCCFMSDQJ0XAiueeEEMKERJMQTUeB4EIIYUKiSYimI0uTEEKYkGgSouGo5IAQQtiQaBKi6cjSJIQQJiSahGg6qgguhBAmJJqEaDhRV+45IYSwINEkRNORpUkIIUxINAnRdBQILoQQJiSahGg6CgQXQggTEk1CNBxVBBdCCBsSTUI0HbnnhBDChESTEE1HliYhhDAh0SREw4mQjnsKQggxEUg0CSGEEEIYkGgSQiiuSQghDEg0CSGA7vq4ZyCEEMEj0SSEUFVwIYQwINEkREOJ0oEAcPWfE0KIkUg0CSEQydIkhBAjkWgSQigQXAghDEg0CSFU4FIIIQxINAkh1H9OCCEMSDQJIeSeE0IIAxJNQgi554QQwoBEkxACUarilkIIMQqJJiGEKoILIYQBiSYhBCBLkxBCjESiSQghS5MQQhiQaBJCqCK4EEIYkGgSQsjSJIQQBiSahBCKaRJCCAMSTUIIlRwQQggDEk1CCLnnhBDCgESTEEIVwYUQwoBEkxBC7jkhhDAg0SSEkHtOCCEMSDQJIZQ9J4QQBiSahBCKaRJCCAMSTUI4mPv876B1353jnsaOoZgmIYQYTWvcExBiElm8/f8CANz/2nvGPJMdQpYmIYQYiSxNQggFggshhAGJJiGEGvYKIYQBiSYhpoTZu/8MnW99xDdY7jkhhBiJYpqEmBJ2fez1AJxxVhJNQggxElmahBCIFNMkhBAjkWgSQgDd1XHPQAghgkeiSQghS5MQQhiQaBKNJ1o+hLk7fhdIu+OeyviQpUkIIUYi0SQaz8Jtb8Lip/5PdL5587inMj5UEVwIIUYi0SQaT7R6LPv32rExz2Q8pIgQKXtOCCFGItEkRJxk/26qiypuqeSAEEIYkGgSIu6VK2tqMHScSDQJIYQBiSbReNKeaGqqiyqN22qjIoQQBiSahIh6lqamBkNHCbAu0SSEEKOQaBKiF9MUrTc1pqkNyNIkhBAjkWgSjSd3zzXV0pTGidk12br/bmB9peIZCSFEmEg0CVEEgjfV0mTLnosP3YO9f/08LN76azVMSgghwsMsmtbX13H55Zfj1a9+dZXzEaJ+ojwQvKmWppYpczBaOQQAaN/7+aqnJIQQQWIWTX/6p3+KRz/60VXORYixkMrShMhy7VFez6qZ4lII0Sc58DXs+90fQnLw78c9lVoxiaZ7770XH/3oR3HFFVdUPR8h6qfpdZqixBbPlbSzfytoXIjGM/vVdwMAZr7xvjHPpF5aloPe/OY34xd/8Rdx9OhR00mjCNizZ77UxEQ1JEmse3MC8UL2fcx2gA753ezkd7lT98Z6jnguE0FJewZx3B09Ll3Mjofh2ClEz0646N7UTzw/BwCYnYm2XTen7d6MFE233HILTj75ZJx11ln49Kc/bTppmgIHDy6VnpzYefbsmde9OYG55RSLAJaXjuOo8bvZ1/v3Tn6XZe8NO6f5Y6tYALCWRkhXVvDQiHHxkVWcAqC7ttLI35CenXDRvamf+ZUUCwCOLx3D0jbf/aTcm337dpmOGyma7rjjDnzkIx/Bxz/+cSwvL+PIkSP4j//xP+Itb3lL6UkKEQTqPWdroxL1vPkNrZwuhBggamYnhZGi6ZprrsE111wDAPj0pz+NP/7jP5ZgElNFGmduqmha6jSl3b7AsRwet2wLXxRl/2rYIinEKNrf+QRWf+ApQDIz7qnURj+Bplnrgeo0CTFtliZ2EYuMlqacaRGXQuwAyQP/C3ve81IsfuJN455KvTS0KLApEDzn/PPPx/nnn1/VXIQYC4WlaVqy57rrQEIcn7SAFfu1N7bdjBBDiJcfAgAk+7865pnUS5q3n5qWzaYRWZqEKGNpStOdncsOwC5iaWSs01QMmBJxKcQOkLupoqaV4uhtNpvW7FuiSYioRJ2mEP35rKgxVgQvaNjOUohtaWhsT7HZbJhYlGgSohc07TIzh7hgsO6zuEUJoSjtkhMSYoppaHHcflhDgGtghUg0CZHjePhDXDBYN0GWPdesBV+InSLttRdqnnuumRY2iSYhcjwPf4gLBp09l4RpMRNiEmioeGhqnSaJJiF6uB7+ABcM9jrSuI2oYcGcQuwYeSB4w7JK06SZYlGiSYgch7UlyF0WO6dYliYhvKR5IdmmZZXm2XMNSwyRaBKih8/SFOBCSYsmMntOCDFAVim/aeKhiOVq2Noh0SREjks0hbdQ8u45sk6TEGITQVqdq2TaOikYkWgSIseTPReiSd7VRiXA6xBikmiaaOrRNLEo0SRED597rsJdlrceEnsdCVenSQgxhIaJh4KGXbdEkxA5rjpN1VhoWvd/Cft+75Fof/uj9Fi6TlPUQoTUL9KEEI2zuBQ07LolmoTICcjS1PrHzwEAZr75IX6wJ3vOM04I0aeh1tqmxUNKNAnRI6jsubxgnqN+En0dReqwRJMQXhrbXqhh8ZASTULkOHZMle2yctHkqZ9EZ8/lqcMSTUIIElmahGgmrvikinZZaVyiRYEnew5oXnE+ISaF9WW0v/fpcc9iKE3bbEk0CZHj2TFVVUk78scZ0eKvcAU2a8coxKSw+Ik3Yc/1P4nkwNfGPZXNNKybgESTEDkOS0tlu6xenFEdZRAKq1bDFj8hJoXkwFcBAPGxB8Y8k800rW+lRJMQPVzxSRWJptSR0ZbmAd2s+FP2nBBhUyRrBOhCV0yTEA3FFdNUraWJOn8R0M1amkpYtYQQ1eN8tmuhYbGQEk1C9PAsSFUJDU8geOrdjRaWpmYtfkJMCu5nuwaCFHIVItEkRBkqtzQRC1LkbKDp+SwhRH3kz3aIcYcNs1BLNAlRgspcWomj5EDhZuN2o2lvQQ6y+bAQoshwDTHoumlufYkmIcpQ0c4vdVh/+sHjrKWpV3KgYYufEJNCWjyjsgaPG4kmIcpQtdCo1NKUbhhHfVZTW0YIMQ7U6igYJJqEKEHVpmkmyNJraXJVH9eOV4ja6D+jeu7GjUSTEGUIydIU9R5nuk6Twz0XYGyFEFOLLE3BINEkRBmqtjQ5WpvQu1GHaFL1cCHqQ5amcJBoEsJBbQUhXW1UyOy5oo0KMU47XiHqQ5amYJBoEmKQNLUdl7vCghRN5G7UUd+paWnGQoyVRBmuoSDRJMQgZFZY5YHgDjcYXafJU21Yi7cQtdG3bMs9N24kmoQYhF2Uqo7tqcPSVPS1UvacEEGiWmrBINEkxCBsb6eKe0F5AsH53nN8vARrzRJC+JGlKRwkmoQYgF2UKl/EHDtLdk6pLE1C7AxVbSZyS5NKfYwdiSYhBmFrHBkXyYVPvRknv/NH+fl4xAl7DZGj5IDcBEJspip3vdqoBENr3BMQIiSi7iqM+XMZxkVs/o7f882Hm002hl1Y88wcZsGXaBJiM+trQDKz46etrcSJlzQFomjcs6gFWZqEGIQ0r1O1jeqCzZ6LPG1UAl28hRgjlbnrQ7c0hTqvCpBoEmIQ9uH3BGpXjTN7jnPPBXjdQoybqjYTvbpwwVqaGpQYItEkxAD0ohSgpYnObPNUG27QIimElcrbCwW6WWnSJkqiSYhBSNHEiyw+RonGmz1HtVFpziIphJmKs9vCtTQFOq8KkGgSYgB6Uar6eA+sFShvo0K4GoNdvIUYJwGWIKmFUOdVARJNQgwyBaKJN5VHWdNextJUtRtCiAmk+gbeYVp4m7SJkmgSYpCK3XO1+P49cVZRQs0tUpE9ITZT8fMdrDgJVMxVgUSTEAPQgZzs8TUsLp7WK2ncJhv2NmeRFMJK5SVIAn3ugiy9UhESTUIMQlpQpsbSFHOWphCzBoUYOxWXIAnW0hRi6ZWKkGgSYhDacmQ7Pq/oW0vvKM/CGrdVp0mIslQtakIVJ6GKuQqQaBJigMqy5+qo6NsrZ+DZjaZJm1uQG7RICmElxAbedVB5faqAkGgSYpCKAsHTpNM7voadotPSRM0t0MVbiLFSsds6WAtvg9YDiSYxdURL9yM+8o++wbSlybhIFpamMN1zadwi3XPNWSSFsOJJwqAI9LkLVsxVQGvcExBipzn1HecCAO5/7T30WN49Z1ss0jhviutYXMgO4i5TOW1pas4iKYSZikVNsJuVBrVVkqVJiEGqqtPk6e+WU0MBzazkAFMRvDmLpBBmKo9pCnSzEuq8KkCiSQj0+8HRlqDuiu0TeqIpWrcdv/Ezaqg6HrdkaRKiJNXXaQrT0hSsBawCJJqEGIS0oNRhaWKFnGsBSzpcOYQGLZJCmKm8Inigm5UGrQcSTUIMQluauJIDvpgmcvfq+IzUYmlKBy1yzVkkhbBSeXuhUJ+7UOdVARJNQgzAmtet7rY0KRHTRGbkRGmXD8wkY5qatEgKYabyQPAwLU2hzqsKJJqEGIRNGWbdc46YJpdVh1zE+JIDzVkkhTBTdZHHUDcrDUoMkWgSYhDW0tRd3eC22vK0hXuuegHk+pykzQWpN2iRFMJK1W7rUN3iTdpESTQJMUBl2W2lAsHrsDRxveeUPSfEEELtDVc1gYq5KpBoEmIQ4uFPo97jYxAQpYpbesawi3fcosRZqDteIcZJY5+LBl23RJOYXgxusxOhRE1ee8kyptd7rhZXG3hxlsYdc80pAI1aJIUw06DGtYM0SSxKNInpxVNojhAbKdNPLrc0ecz3dVin4haXLt2gRVIIM019LhrkrpdoEtOLQ6C4LE2GOChKYJ0I4zJE1JsTee1Ji2uj0tAdtRDb0aSA6EGa1FZJoklMLa4FjBEbTHB3cWzFQs7pBszcc6rTJEQpqi5uCbjCDiphcB4NEosSTWJ6qTh+KC9YaRE1zLGbYObkDTinA8Gbs0gKYaYOC2yIz16DNlESTWJq8VmaiGDoomCl4XOiJPu3J6i7KuvXACkPQ+J7AAAgAElEQVRbEbyOHbUQE0YtAdEBCpQmueslmsT0Qsb1pIgqt+pU3nuOyegbJGlT7VdkaRJiCDU8F0E+ew3aREk0iamFFw4dMn7IEafkWVyYjL6kF3DOCkY25irEhVuIMdNUS1OT1gOJJjG90MKhRbnnUocrLGJqIRVjiEUy7vTG8DFN1Gc1tfKxEJsYDIiuXtCEaGkKcU5VIdEkphd2AYvbpECxlxxwzwnw1Y6iK4JzlqYmLZJCmKnDChSiK6xBmyiJJrGB1vc+A6TdcU9jR6CtOnGbqortsTS5MvqYRdLjMkQ/u8+8+Ek0CbEJ62bi5Hc9HXv/7ALfhwT47Hks6JOKRJMoaH/7o9h7/U9g7gt/NO6plMJbSDJNWEuTJxC84uKW3kBw0j3nqmwuxLRjfC6SQ99C66F/cH1EkC1LQpxTRUg0iYLkyL0AgNaBr455JiUpWpY4LE2MGHAFglfcEqVMyQHmsxq0sxTCSj2B4OFtWOi1doKRaBIFReZVgA8lQxrnVbE9liYmfiiPaaq2KCRVcNMTZwVQpQpSshCmEI2hlpIDAT57E/7OYJBoEn2cVorgKFEVm3r4C0tTOIHg/TmxliZCMLMWOSEaQi0JEgEKlCCFXEVINIkCdwuOwEi9wdBxh4rV8ZUcqNbS5BeMuaVp9GelcbtRgZ9CmKnBTRWaQEnjdi3XHQoSTaJP7taadCtC4aKq2NLk+ZyKY5pS7z0ssucMi1/SnnxrpBAVUEuCRGjPXtya+I02g0STKOg3lQ3soSShg5rzcaQYoFxaOS73HNFGJfFZmlLa0rQ2NaUphNgxGhgIniad8IRchUg0iT6xI0YnRNyWpg4XQO1I7/e55zwuQ1/JAdPi5/0MIaadGtbO4Da1cVvZc6KZpEmvBceEu+fSMsHQjKXJEztVcZ0mgL8OoH/vLdfSL4QZ2OItxBhJo7gm91xY63PKhjVMOBJNoo/H3RQixXWQux+2YS/h0srx9Z7jg7r5kgN5bStj9hyaVQVYiJHE7QrXzn5/O6pDQB0kXALNpCPRJPpE2c8hNFNr6747gbXj9gG5xYzuPUfumKIYKSIuc6QWSxMfqM249VztY4SYclJ20+UlsE1tWqlYDA+JJrGZgB6A+Mg/Yu//uBS7bvkl85h+01pO/KVxG1HatQdeRxHd5Ne1I2PvB1mkEwBnNUucMWNCTDOe+mWeZIrQNitJu1FrgUST2ERID0C0dgwA0LrvDvug2Glp8sQoxS1uoXQseOz98MQYpIRLs19xPSyLZNOIlu5XBmNAsB0FAPgaeAe0qQVyS1Nz1gKJJrGZgB6A1JEJl/bcjLxw4LPhsjIFjGiqwdJEFunMxhAtYcjmvmLniQ9/F6e+41zMf+6t456KyMmLPKbp6GNzPPFJoT13jnCASUaiSWyiykDD9j234aQb/oXdBebMhAP8VbEpy1HM7S5dJQdoN6Mjm4X5nplCmKIS4qP3AQA63/zQmGcictK4jQgpkNrrqtWSGFIxacPcc61RBywvL+OnfuqnsLKygvX1dTz3uc/F1VdfXcfcxLio0NK060P/AcnS9xEfux/dhYePPD6NEgDOTC3WrZX0LU3WvSJvaaq49xxACzlgMLjb7p4LbfFuFFNSHmSqGCzbEY98tWa4YhwDs+rkbZXSNIvznHJG3tlOp4M/+ZM/wcLCAlZXV3HVVVfhwgsvxJOf/OQ65ifGQKULMW3NicjjB0Z6CzzSlqaKe895xB9rLWRcbglRCFNUQsoUIxW1MBhKkLbmTGOYDVoxJjAL74Zs2twKPcWMdM9FUYSFhQUAwNraGtbW1hA1QE02miotCM6Gsi6rBh1AbS/w2B9DBoK7dpZ8zSXWMucpORDa4t0oiGKkoiY8de48z1Boz13xW2yGgDfZENfX1/ETP/ET+Pa3v42rrroK55xzzrbHRxGwZ8/8jkxQ7CxJEm95b6Kl2ezf3dXK7l/cmQEA7FpIAMtntHs7tvUV85ySdubSm2mlaBnGRPPZQz+7kB272zS3FDMzbcTtGcRJd+Tc4k5PLKbr2HPS3FAz9uC9iY7MFH/fitZN154kcfY5nVkgTkfPaTYTP3v2zBfzmWsDM1uNi7N70ZnL/v/iXIy0Qc/5ds9O/ewCACRpdc/qJDHWe5Nmz0Uyk62fJy0kwG7bXGxrDRDNdYo/z3aATgD3PDqQrVHtmezfe3a1gNnN8wrruSmPSTQlSYIbb7wRhw4dwmtf+1p87Wtfw+Me97gtj09T4ODBpR2bpNg59uyZ3/LetI4cx97en6u6f3vTBC0ARw4exlp79GdES8dwKoAIqXlOu1fXMQNg5fgxHDaMmVlawW4Ax1aARQCHHzqC9RFzOzUFlpfX0E4TdJeP49CIz1lcWUNusD944KH+7myAwXvTOrKMvcisX+sry6Zr37vexdrKGuI0BlaO46ERY+aPr2IBvXsdt7APwPGlJSxtMS4+cgynAFheA+YAHD10BCsNes63e3bqJj68glMAdNdWg5nTOBnnvYkPZc/FWjdBB8Chg4fR7W4/l329fx8+eHjkWgMAs8dWejIZWD66hKMB3PP2kWXsAbCyHmMWwEMHHkI6v9k9F9Jzsx379u0afRDI7Lndu3fj/PPPx6233uqalBBMj7PS0BXBHW4nV20Wrr8d3xLFUXIgirLsH1PDXrmGQkGtbAIi4UuW+Bp4B3bPk2YlhowUTQcOHMChQ4cAAMePH8cnP/lJnHHGGZVPTEwpuWiqIevH23+NEluxMeg6HewdRczL0aLA07C3+CzDfUlVETwcdA+CoYgLpNoqVR+rWTVMPOQ0MNI99/3vfx+vf/3rsb6+jjRN8bznPQ/Petaz6pibmEI8BSTdsP3Xih2TfdFL41bFliaH1Shpu3aj5vIJ09LYeQpQMH5AeCxNbA22KA7PopMQzb6ngJGi6QlPeAJuuOGGOuYimkBhpViu/KNcDXsBTmwlbaDX6sUKlWbsaFHgadibfVbL9J15sgxFNQTnqmkwKVFOJY0SROm6qwZbaNlzTVsPVBFc1ErxgNWxK2HFhsPtlHqadNIxTY7ilo7v19ytfMLdc/Of/W3s+ZsXjHsaYtpgYntin3UmTToBWppq9B4EgLFsqRA7hKcprhPe0uRp2MsHglPHxx1fDz3P92u8lkmPYVj4zFvGPQUxhVCWpqSTWdvpGmyOTVrFuGK5JhhZmkStFC04KnzAotz5Rfdsc1qaWPHAnN+TPefsBWUOIK8zLk2ISaF4LgzPq2OtAXrrQWgu2YatBxJNol5CLjngmZsxDmgDbHaepx2M5/s1mv7TGq2FQkwKxXNBZKDyZVE64VmaiutuRkVwiSZRKx5rjhd6F+fICqMb9oLckSUdRGkX6No7p6dml97GcHRzS5ga76EQEwPjto75TF0gtzQF9twxFrYpQKJJ1EuN2XOuzBSQYsATdE31tssXYqbgJmn9ylu6WMsnRDHSKDZdR+ebN6Pzjffa51InKdsqVYitSYlAcMYqtYEgs+fs15Ic/HtExw5UPKNqkWgStVJneiq7I/MEOHtimtiK49kYNqPPsbAypQqMQeMnve8VOOmDP8vPpQ4C2rHP/q+/wCl/+AQg7Y57KsJLbqlmrLXsGpXwiSGVQ7jrT/7zC3Hyn19Q8YSqRaJJDKeqXXi+G6ul5ABfFBJgs9uqtjR54qzaWTA84dIDertf47W4Si2ERkDzX7z11xCvHgHWarDAikpICTeVO+PMWU6kSorkHuMaFS8/VOV0KkeiSQynot1MPwCyehOzp74RAO7aE75lic/SRFQp9wZqx0RmTohZPCQhzT91xriIgCgSSQzrgbO2UZZNG5iwZixsU4BEkxhOVQ9AXKeliWyj4iw5EHVXOMscI4CctaMAp3vS+J2lcWfis2WCakHijXERwUCtH4XQ4Jtxh+aeY2K5pgGJJjGUqna8aZxkf6jhhUVfQ5wgReQSKEjtrjA2ew5wxDQBrpILZiHh6bkXCGmU/wbDmT/j2hGBQiVtZMkXbLkSV4eAqpnwYrcsEk1iOJU9mPliUcPLwdN/jWxT4HKFuSxNxPcV8y49oLdjNI7J4p8m9AWfBChQctfOpH6nwtWGyZU9F9LvFnC3hJlUJJrEUCq3IngeMDKzKOqu0QHtWa0isvgkuAWDWlw8C7Gn8TBAWpo6PlEaAGkyk/0hIIFSZ/0yURERb6nm6zR1gvuNpEUsVzjPU5VINInhVPxCcYmyOsYwwdDgYo48ViNP9ly/BoyjjYz1cybYPee1xFVKnZXyRTVEoC3V9OYx4JIDdGeECUWiSQyleksT/8JyveQcXcQpCwqT3eZ5WTuy5/qB4B5Lky0zZ5LdcyG2gSmCaUPLjBIUbCkOT7JG1F0NqjBrmlvYJnQ9YJFoEsOp2ATsiidxzIkWf9ZWIj0YV09aKqi74jgrZFYt89ySDifKQiraGAcoUIqYJo/bOpwXaOOJW9zaRjfjDtEiGZmL3U4DEk1iKNYHf/Hj/xlzd/4B/wEuAeQQWi73XEXp/UVBO+Jl7SkGWogCx4Js/I7TuMXFMAS0oKZlBEpFpGVchg1xi0wCWTJFtZYmAEH9doHc8tyM36FEkxiOcSGe+9I7sfjJ/0KfvjZLE+2e49qipETWUxpFmTWHqgieB3UzxS19mVhZM9A1m1WIsUohsPghZx2rSklKFLcM6bttOnGbs8DSLvQAMz8BOhZ0kpFoEkOp/CVXh6sN4DM6WEtTzz1n/b6YViXZ+R2WJne1YcbVSPSpA4LaGfdFZTjuuSLg3xPr15CX1SRAx/rRyRrhWUkB9BoJBzanipBoEsOpeBfuEkA1BI8ztYqy48lMtbiNqGt/WZfJuKNjdihXIxe7EWL8UEip2/14t3ossKIiyE0XvQ46N0RVk1mpw5pTVUg0iaFU/kJx7ag97rmKG2KSL7s0mSEtTY7WLs46RJSrMeZiN0JyIXndl5VSoidjU15WEwFrcWHDB7yNfquGKVcy4Ug0ieFUbPL37airH0Nbmtj2F0mba75L1WlKi88AHMKXKW+QcLEbIb3YQ2xZUs7SFM51NB02JpL+DQba540OO5hgJJrEUCq3NHmsRq4x/KJEuZJIq05qLU6XniiArNcRDVTo5Vxioy1U/dR2OnsupBd7gNlzKBGrEtoLtNFUXNwy1Ow5OhY0pBIkJBJNos9gvZeqY5o8D71rjCPQ0pE9Z76emLQ0eV7w3pID1pimKOplzxGWpoBEUymrTkUopmk6SMk6b/6YpnB+uwCxGcwJ6NljkWgSQ6n8heKJ3WDERt4Y2FERnHOf5bEo1kraM9yCEfPmeG8dIiqmKSEbh4a0SIbYlb1MTFNIQfZNJ+5wgsZRgBZAeEKZjAUNacPCItEkhhOipYlZjLxp5aRo6pccsFbS5szYaZxkf6DmVKK4pXWc0WKWu/xCciH1u9GHIzZSsnTFICF9t02HLcVRadxhjUxysVsWiSYxlMqbL7rKB/AZZKbFZcAtmQU0EiUByJIDWW8q5mXtKIhZzImNabIHnVstZv1K1+EIFDitkJWSFzGty20tqsHsfu+tOaRlMcQkBgC9zSZjaQppPeCQaBLDqXgn42uJwje65a4jApIZsrgladVJZnxVyqkyBU7rDtPKI+kgSteB7vrI4wCE+WIParebCzn+ZRLcC7TBsLE9vKUpzOeJzToOyl1PItEkhlK5yb/ilihFYC27k6OLW+bWGaIiuKsfHnEdUdwLSK2wTpNRlKaBuhOAQHe7rqKvYb1AG03c4oSQt/dcUIIfdFhDUFZeEokmMRz2JUd2Wo/SERaKYVCNdJ0FDNmYJjZTjT0/+Cag2by4LD0AnNWscH+OEB55GYMQrSEBLty+hr3hXUdTSWMuQaLSZ7RG0pgs1RLY/BkkmsRQaEtTDQu3J1WfFihxO3v4rSIwTpBGifkFnMU0VdwPDw5zObhCmtb4p5Rx+dVMkG4tlwU2QItZUyFje2hLExF3WCtJhxOLIT57RiSaxHDIxdu3QyaDzT3uOTpmILeMEHNL2vYXl8vSxAutNJlx7GKJoG2rhS3URR4IcrfrimkK0GLWVNjNSpR2R8cFDlJsQsK652yplhA3UVYkmsRQ6J2A+SEYsOCwwoxyz+XZSM4MMsqqNWMWBWzxTAC92i8Vx0FhMOPQbmka9ZIPsZBkToip+q45BXgdjcWxKfIU0w3N3U2HEAQm+hgkmsRwaEHjqS9TlTAD+qn6vmBoalxMLJRk77lsTnxfJ597zl5g0SwuQ2yOmzMlcwpRkDaVYs0hYjy5GMrqLE2t++9G5x8+5BtMtp+aZJdya9wTEGFCL8Q1tH+oI66n73ZahnXZSwkh5HKbOaxGrh2vKxB8RPZcyDFNAe52faU4wruOxjKY+JD/eRSeGmwV3PO9f/08AMD9r72HHlvUbUvTrM3SKCb4NytLkxgO+aP2VTKuWJixQZnwtSChxFnsKDngyJ5zWZqokgPGY3M3aYiLZIi73YqLvopq8bijKatLqJuQpIMIqTkWNLj5E0g0iaHQ5tNaLE2eVH2+jQrAmsztbsA074JOdPlOHdlzdLf13hjAaNEjW5GEaI4PMqbJ9RyF9902lWIzsUbcE+bYKOq1LAnrt5uyJV4Ci8likGgSw2EeZDgtTUy7AYAPHvRmqgFkKxW7JciVMuzJnqPbtQCIkqzRMdFTzjyvEK0hIe52PSUHAnuBNhqXpcnhrg/itzuwNpOxoGHM34dEkxhKLTFNrAvQE9NEW1scjVMZceYI5EyZQPNiTo7YqSiyXwv5cghxkQxzTg6rUYiCtKH0NxNEULTHgh6SUI6igczbEdm0ZXosBoJEkxgK+yBXZ2kawBHU7aq+DfDBmcZF0lfSgCscV4xxLEzWWKiUrYQeojk+pBdPjnVOA9lZk1wocOrwuPddlqYKf7tkdweAiXEMNCaLQKJJDIf8Ubt2yJVbmoiikzmeRY9p8ps4upS7MuGcJnxjLBRbLybERXJq5jTBu/ZpgylZkkbZ65ddo7L+lRX+dj1ruXHd7IuryY3Dk2gSmyhaiTB46jTRliaHq43NOou5AOdiDFFyAIAjO49cWNleUMU4o9WMdWOG+GKfkjkppikcqFg/bw0zj7uewHNuu6WJSDYJFIkmsQlPurrLXMx+hsNFxdc3IgOcAWdME2nJcrRRcbmfjGUazItkz9QfplUnvN2uL6YpvO+2UaRDAqIN97GIAyKTbtiWJTROCzVgiWmK/Z8RCBJNYjPEQ0lnUQ1A7zZcGWTOiuAV1WnqF6djYppm6IXV654zXwsbCB5i3E2Iu13HnIL8bhtJRK2HqSfpBM6ivQQuC/UUJIZYkWgSm0iTjvklbe1BNhTamuUpbunsPdclxjE1kZzZeb5AcMfCZKwJRZdmCHCRDLEopC+mKbzvtrFQVfUda01vXLXuOX9Mk70EyeT+ZiWaxGaSWftLukRvMdrSRJux+ZgmsFlh6GWSWS1NeX830pIVdde4buge12T+WRXENIUYw8D2CKsDz/cUovhrKp6q+i7X+/pxem4jz8sWqBwydpLXAysSTWIT5hcnBgKnPYt91QU0Xc1xHQsHk6XnaAictngXKCPkNmAtVRDFSOMWEQge6M4yxMXb2IqiIMDYrMbCWN69G05PDTYDXhGXjR3ouWdA7jkxVVCunRIxTazLyeVqczetZSxBM+bjfaLMVjhu45gOorRLv4BToiUMCGEW6iIZ5LzY9kISTcHAxCn5Y5ocMY4WWnxhzgJWcIX43BmRaBKbIV64KdmDbBC+5IDD9++JBSI/iyo+6akI7i2ICTjKNDDlEzqIrPEYoS6SwViaBopV1tH3UVQC9awyxw64kT3Fbi2UszRxa9QkJy9INIlNcA9AXqDN8RCs2f3yKSLiM7IFxuWicjXsbWcxR4YmvD5R5gweh8c6Zzf9Z7EVkx3DEKSliS36arzH7e9+Eu3vfMIzI2GFWD+K0AZiHQSIVkcssW/NAMCvNxMchyfRJDbjcQdV3UaFLfDI9FHbMI6M1YFvd0mJCIcAcrkB0VvIzZmAtppO2TwCdSEFKJrY36z1+D03vAR73vNSz5SEEWpTFMWcOzz/jNYMnRRjO+9s9gdXyQFjmEbPYBbkZsWIRJPYhLX54iAecyslAlqzrqDuqLvKZ0jFZENMQgj1M1Q87jliMfMG6FM1upiYJuM8umto3XuH7Vg3g33bAtzxMoI9Sip5gQonpFXYG3dZRRxbGfccbaGXe05MFR4rBStoopgbE+cCaLQLbHAMAEeNI0s23AkxBoDR0uSpCD5Lj/HWz0qJmCYqa9B4zvnP/hb2vvtFaN33Bdt5yxKgBYy1wCoQPCBYS7XDGp4ms9Xc8xLlY1TcUjQaz46DLwcw43Q38WN8dVAsY6LsX1TrBF7I+QLBnVmNY45pah34KgAgPvJd0/FlCbHGEVWOgnyORA0wdds8fSUrqgheqlBxPPnFbq1INIlNuOJhXK0AGPcc36fJG9fD7v5sLrd0w7FUyrBnMXO65/iYJuvOctnkJvWmYbsJUXCw4niCX0DTCJVV6qm5lHQQpet8PS/DXAC+fl42KKKSSGRpEtOFJ6bJY2J2CAe26CTg7O3kCtQe9TlRUQuFsyY4qpTXIBjpIFbLIl/CReAhxMWbmlNrppoXqHDDCFmmkPCG8wM7/oyUPS/V6zPA586KRJPYhMut5Xk5k24Idk7uRSAmYnUw2ITXILTiVha867qOiq1TGFjETVYh0k1AFfyrxwLEpXvXBCHY3eJYVAfb8NxdFmVnn5Fi3fcGaTOW5xATMIxINInNOOKHaGsOmzbrrIqdjakqpqkHW98kmeFe1o5r9xe3zNsh2KxCO51hWbt7LsAsHs/GQHFN4ZAmHfva5gwEByq451GS/duZjUkV3ZxgkS/RJDbheXHRu5OYjBtyWFv6QdfVpd1nn8OnGVdec6mMYIQ1qJ0Tl6ZrLtPKwUEl7SjKUrVFMbAmxVMHXbbDEQgO7LzwiLLEFrcYo2KaAnzujEg0iU243EEOS5NHOHDB0H4XFWWhyd1z1v5zrZnKK4K7ajvlc7OOY3fJjHuuLjET4OLN9j0EQLpJw7vmaaKOQHCgQmusN6aJSgyRpUlME9RDmRLHDoxid1gtv3DgFyU2pol1z/mC4F2Ckbay2UVLVtPJfn7bOetxN6Vxq5bPcUFZmniRGeQ1TxNkILhnwwmgsqKmXkFDXYsxbjJEJJrEJuooOQCyQFs/rZ93UfGtRLjdX7HbN7oo2do6pVyTHjELIFo3xFyxMU0m61U97rnaY6cIfL8N4jpCdElOEYzFxdXqqUyPOAN+9xxhaTI2hA8RiSaxmRqKW7I7LI8FomiI6So5wKR99wIzjS+jzD1XcXxSyZgmi2hhxaXpnI56XC7y6wwwe85Ti8wkcvPzBygUpwlmbXOVHGhVFAieU8rSNP0WT4kmsQlX/JDj5Uy9GB0BwqwFqCBpO+NKmIwZxtWWZO6kOrKqGAFIiktLxmBt7rmK0rZ3hIrd1kFe8zRBV9UPJBC8Rx2B4AAmNrZOoklsJuYFCv2g0RlqjkrajhdK8VmuQG2jpSlxdCmPO5wFwpmFxmXPdRB11zCqH2DaS2U2fT+9WKPKF9QoyopzBrhwM+UoXAH/AV7zNEGtHwEGgnufCauFLe21nwoyc9WARJPYhCuAmm3XQWaQsd3DgQELEPtwsv28CpeS7WWXxTRxbiEqIwdwt0RgYmTML2zKEpcvqNW7zehaYTVACzmPKz1Al+RUQVmaHIHgVcf9ecWYdSNcbGYn83co0SQ246ha7eqfVHnDXp+rh60/RKfJk+UWAEccVBT7LCkt4jszCllXnFIdcTeeXX7F8OUouHg6oETFZ2Gi8kDwil3L7uw543pTVRuYupBoEsMhH+aouzrSTTNImmfPGdNOXdlOpAWo+KwWl9mHuJ2ZnI07J1/rBP4FT1vzMGids8Qf2WIrPDvjOtxmbBZjLZCxfv0CrsTLKjDr2rRBB4I71k4gvEBwa4xj5fOvGIkmMRRPVgezGNPVul1p904zdjKTxepYU2KjiLMesUHwIFszlPocxtJkdA25GkDXIJocorJqaCFH1dUKOPh9miDWTo/VJdRAcLOFreI6U1Uj0SSG4rGGUD5qOng6Lx/geaGw8UP8Q80Ed3ssHKnH0uSInWLui/We9FOkA6sllHSCi+/h3XPEC7TuausNpVg7LVZ0j5CtvCJ4xYHgE94vUaJJDDDwkDt87ZX2UytidJi4nsgnUJi4nnwMUazTZeFgA8Hzz2EtTcQuMDezm91zhECpyz0XXFwFax1kewUCwVnXpo6kgwipyVLtsjRVXMvMLcaM7wyJJjGVeBpJUi9ox67X1dyyNetyzwGk5SxI9xwZmwVwFkBrkT2HlbAe0eT4firGWy3e9LJybAYED9W9wGM1ih1lJgj87rkZW5xqi99EhYREkxiOwx3E1Zdx7HpbThcVbW3pWVAqc891sgwmpvdSMusKBOdLGxAvYbP702Hxq8OF5MhirJrMCkncs7jVS0KwZzvK0lQxuRAyBUU73HNOC7oZt6Wpt26OuG5ZmsRUwgSCu1qcuFxgDuuXM34I4HuA8cGf7LU76rnQRTRbSKPY1vKEablCzqWOGi6u76dqyGbOaRSZA49dBWIFDVcg1pd+71kPrLhLDhi7CUg0iemE6dSdW2ZMD0HaPz/4YpW81WiGNwN7ejsxVgvT+U+wQnnquXgsKVHUcx8avjOq5Qo3l1rqJ3kyRCvGF4M3S8WghXbNoZE8+A3MfvEd7vFMXKA7qNuzHhhhSsEMklrdbsqeE9MIU6CNLu4In7UlKwXgySBzWppYdyORPQfAdnwUFWP46+CsFv1xts9ivqeUFXC11WkKKxDclSRgTZCIs3Y2wQW/B8aed1+GXbe+Aeiu+05AtiIC4KqnVqn4dRRAtVqQZGkSUwll/nUETntatbjcKWyhSrCWs927q6kAACAASURBVN4YIr3f5/5zBME7moEy45jviXbPEcX+vNDxQ3XgtKaGFmQ/yRSbgLJuqu2+59RvcQecoQoErs1EYXke8UxJNImphIlp8mRDeKp1O0zSLqHlqWdDZc85TPIeq8ioOW1lgm8ZX9zMPZyC6tt14Ckqy8a3TGrPr7pIHZvAYeNHr4fRQJwZX0+t0ufJIZrMlqY4yeImA3v2rEg0iaGk1rgW+GIlioWFMAP7Xij+DDK6+rjVPdfynN9nabKJgmjzOMY9Z/h+PZl8VRNiGxWXdTDEzMRJxlkUt8BqcQGQtuayP7isyDvsZh3YRLnc+kXW8WixGOQmyohEkxgKVd/IU//FGwfFPmje5rhg3Y2EG9CzKDsrgo/fPZf2zxnYyzrI4pZWK98A7GZiUl9WdeHZ1GwYT7UicrqqHOVXKDzXTmYNhraJsiLRJIbTmiUsTbx7zpVqW7t7rprilt6YJraxJ3MPN44zLsjW74ntzZfjyOChaM0gStftPQZrIM2THdgGrpR7LjChGBgpYSnabryp6bXzs7LuCGHFNDHX4ulWEAoSTWIomeXEKJqY1PPi/HxRN091b0/VZ9/1OLLnHHWt6OB08gWcj7Nl9sWEK8+RyVexRcTTY7ByEj4JwR5/mPLnbiCe7Nmh401ua7vA2vgZ1dVpAnzWyP66Zolx5Iv1hoJEkxhKUfulqqaTLpeep1YRb21JXS8upk6TI2PG4850uhnoTEBTjSDeVVj1ohpi6rOnrxgdCB6SSAwRT522YeONCRKuz2rZY05duCzU9s1miPGEViSaxHCS2V7TSUMrgKiFNG7xgdMAbzmh4z18zXEBvsJ51F011XZx1bUqFnIuDor9HACOPnqWQpiORbLqRdURu1Y1LiFHxkFN6suqLopNU0n3nEk8eN1zFcfjeZ6JfkKQ0T03ob9DiSYxFPZhZrLtABRNLbk6TR266BpdVBEA4gRp3HYW67T3bOPcc/xC7q0AzWUC2tyfHsFbtUWkX1gwIDeB57fBxrdM6MuqLkrHNBWWasP4ONtwhlZywHPtXCX08Mp9WJFoEkOhFw62iGQc9xYL0gXmWVy6a3SwbxY/5bDqWMY4s/MAskq5NwtoqwV5iKfW2qbG556rWjTxsWtV43LPkbt2WZpG4AkdGDaeiXF0xF1W6Z5ziSZijZpk91xr1AH/+I//iF/6pV/CAw88gDiO8ZKXvAQ/8zM/U8fcxBhhrSGeLDXWxFxYNdK0aC9i+gwg+5x45M+9D7kTGmxAPCoKzOOeo+IkcpzuudELWjRwrFEsh+ieCzGmyTMnNtkhIJEYImXdc4jinvWP2HA64i6rEE1pFGfV+D3PBCE209YMomNH+M8IgJFvkSRJ8PrXvx5nnnkmjhw5gp/8yZ/E05/+dDzmMY+pY35iXHjcc6wfPCEWFgzWBToO5EXhRo4ZEDPteftnkTuh/kJrLwrJxXPxMU3eQGcqHdgohgoXEiF4Kw10Rfl6PJVQQ6xfSCIxRFzxg8POQRQHpgvw5pZw4nkykcwCa0u+Z49xNXq8BoEw0j33sIc9DGeeeSYAYHFxEWeccQbuu+++yicmxovLPcfulizd2Qez9xwuKpeFBo5ARaqgnSOeyxPTVMo9Z2/WbKvLwmckVv5yd/b9qhJPPE32HB2zZboirOsNkbIlBwBQlmpXzaLWbGYR6q46JrfdXMrGcxkL4zoL74YA4a8A7rnnHnz5y1/GOeecs+1xUQTs2WPf1Yv6SJJ4y3sTHc0emIXF2aIj+q45IN3mXiZJhLiTADNziKO1kfc9ns0Ew56T5hF35jETr6K13ZieRWl+bgZpnP35pPkI2L3NnFoJsJZdZ7RrNwBg93wEbHXdC9kiuWv3bHFM0hl9PTOzbbR7/z86KfucXXPDPyfptBDFUXG+NG5httVFZ+DYwXsTHc7mtLgwk33/x/dk/z2Tbnk/kjhG3Gn1z3EoG7Nrdvg9jGfbAHrPau9+A0C8sIiou4I9J80CUTw4AAAwN9/BbP49zS4ASw9s+T0lrQRAjLnFxeyzFhNgduvvNL8XALC4xbw3jbnrz5H+0PnAKTbrd9JpIYoiLPbumfVztnt2ypLfi8U9xjlFvedivoNofRcipNizq1W4SIaR3Qsg6i5P3fq8k/cmXtwFAJhrdzFjOWc3vxczmMufvc4cZpK1Lde2pBUD7WzOSWcecbS67fyj+Wzd3L17Dtg9j3hXNsc9C/G2zxND0mkh6swBx4G59vrIa4/299bNxZnitxq1s+tub7GuxXGEmU4LaC9ka8wE/g7Nouno0aO4+uqr8Su/8itY7C2AW5GmwMGDS6UnJ3aePXvmt7w3rSPHsRfA0SPHkXYWsQfAkYMPYXXX1vdy73qKtZV1xGgDy0t4aMR9nzu+gkUABx9awt6og/WlIzi0zZjo6DGcCmDp2ArS9jx2Azh84ADWu3u2HHPS2jqi9S4OHlxCZxk4CcDhBw9iPR7+OTNHl7PzHjqO9SQ7Zk/UQXr8yJbXcyqA5eOrONr7/+3jKL6vtbnNYxZX1tDppsV3f2rcwfLRI8V4YOO9aR1Zxl4AR44uY/XgEpJjwMkAlg4dwvIWczp5fR2rK2s4nJ/jWJrdz4cewsqQMfPHV7GA3rM6IJrm1uLsHu1/cIMbND58HKcAOLa0guO98+1OW0iWl7b8TeX34vhqjF0AHtr/INKF9tBjAWDm6HHs7v356KHDQ+d9Ivve+x+QJjN44Gf/98hjgd69SFMcOpbiZGz9/ZzIds9OWeaPrWABwOFjyO7ZoUPbzik+tIRTACwtrSBeS7AI4KH9B5DOnLTlmJPW1tEBgNXlqVufd/LezK9GWABw/MhhLBnOGR861rsXy8WzuTfuYG3paPEsbprvWhcpunjo4FJvrTm67do5u7SCXQAOHTqGbncJs6tJ73k6gHSBsn1sya6VNbTRQozIdO3to8vYA+DwkWWs9Y49Oe5g9YTrHrw3J3dTrKysAXELM6vHg/od7tu3y3ScKXtudXUVV199NS699FI85znPKTUxMRnQZlpHxee0NeeLafK45ypMu88+h4sfYkshmINTo8Egbb69DQAqgNyaxZM6XGGVx+mULWJYAXxGX1Q0fbU+q54q8U2kjHuO66jAx/d4YhxN581bHpWpUVVRNm0ojBRNaZriV3/1V3HGGWfgla98ZR1zEgHAVsX2pMDSYzxp995WGbSo4eIgskWDF4yueC6PYDSOMy9+nqD0mtqohFhygM0qBcCJ4wl9YdVJqZgmouBo1juQj2kCqkmW8ASmF1gTaCa45MBI0fT5z38eN954I26//XZcdtlluOyyy/Cxj32sjrmJMdKvF8OIAL7WCBfYXGMGGWk5ozPiiGBrAAO1XxzX7ikFAdiuxbgr9QSlV12pu7hOsmBqpThLDgDcbyMkoRgsZbLnGOHhTaJBRaKJXJdPHGutCO6pnxcCI52h5513Hr761a/WMRcREnT2nGPnQC4WPmuLN+2e3P2xJRrYay9RcoDfxRLuOWuNIEdGUuUvdocQrRqPyPdsJiz1xJpOKfdcawbR0mHbsY70e2+jX+u5S2XPbTs27R8H8PXzAkAVwcVQ2Je0p4s9awbOYzc8rUQsD/IG2LYw5MuO3s0lbaRRQsZzea1sTJG6nrgcke7usvhVbmmqbrfupd+KouLNxNoxZlqNpJx7zi486O4DqC6mCYDL8tUfa3FLRkEWlrUi0SSGwu5ksrpGnjpN1dZc6he3NIw5IYjaE9PExYBxLy7eOlXSPWcqUmerF+Oq01S1pSlpZ42mQxIQJSxNzHWEJBRDpcwLnVo/PCIl0Jgm61g2/CMkJJrEcFh3iqMBY9qao4SDeXc1aPTwCge2uCXbM8y5UFJj4jZSRLzFpm3Pxkqt1iyXe656MVNVOwo/Ed+Xy/ECreO7nXhKueeYiuB+91w1MU22fpLDxxoKFoNPNAoJiSYxnChfvIkHn01ldsY02R7oaOMYT1VsRyC43T3HCcbsM8gdYBTxjZTBmf6t2Yk+F1INYsZxH6ombc0Cq47NBCWaQhKKYVJfyYGBnprW81cY01TGPWe3NIXnGrci0SS2xLprKI4FuJ5ZrVlE3VWgu059Ri0uqtYsJwJzqw7Tb6qKtjObPsfRSDmv+2N4cVvvSb+WEONCqsHSVHG3eA/eWD+TZTDutfAJTCiGSKl4IaLWURGrWXEvSubcbtektdSCYz0IBYkmsSXU4u1wv9DdxD27E3fJAbK+UxT1rBbGhbI95yo66emhR197O1/ELTFNtu/XszOuQ8x4LH6V05o1CdacDY2srccGJhSDpERMXSE8LNajMjXYQis5YNwMytIkphMqA6SGVOkoRhp3uN1VnCCN2y73HDU3ZGLD+gL2djand5aJI+C8cPfYLU2jvl/W0uQJlDcz8B4r84KoirTNVcpnXqAei19TKWPFYSzvpcpMhFZywBgL2p//5P0OJZrElhS+duOxAMjWI96O7mx67owjU82zkBEvu/YcZU3Izl9D1XWgHwhucc8VFsYRx5KLJNtihyePeQvP0pSSQpd6gU7wDr9uypYcsJ7DZf0jwwEoSpUcmEWUro8sWtkvHxPWs2dBoklsCZcB4hFAXM+sfE581pndbTb4OQD4ejlGIZQmvZgpYzxXcX76OnwB54Bxp20Vl1FMZQll3yV5nz391EKMaWrPcdcet5DGLdItMnkvq7opFwhObLo89cLyJI9txsx/+v9G8sD/sp+zR7mSA8YwjQkW7xJNYmtML7m0OBYg3VmeBdxjbSHcZsUYl6Aj3HOOQp3ea6d3c736RTZLE7ejpixN7LwdMSiVugGdeKxsaTJrus+KaSIgM9oGYb5nj1U7/4wtfyfdNSx87r9j79+8gDpncd61465rt9arm2Q3sUST2BKzVSeKBkSApygfFzzOxwI5XsCeh5oSTZ62KN5r51+QaWuOCyw2xTEQwe+O7ELP7jjEmCY2ELwYY3lBE/FqTcdStHVL2Kr6gMOKPPqeR5755wk6np6MdDZtYM+eAYkmsSVM9+207REZNcU0kXVvsjH89VAChUjr75/f8YJ3iiYkxhc3UdKBssQ5rIO00CDnVBcul6o1SSAKs8xCqPgDogkLrLN8QGZdrCh7Doa5D7FEWd2SRYbuBP4OJZrEljCLa19kLNnP71ksXO1HfC8hgBc1lVqa3IHgDjFhFC2UuCR/T3VYmoKMaXLF4BHlLrxCuok46xVR1iO2m0AxrprfbpnMPPN1xx2kiILbsFiQaBJbQxRog1NkAKCDrX0vFJ+liYkHyixBXEwTNS9XnSY+Sw+wuwL7O0abwKICwdkF1WVpCjOmib12KmkjRJdkoLhFCeOeczaOdpUgMZ4XgO83Yo1TzevaOZ7ZcSPRJLaEqQzrdWdlY9hgaz6F3iM2gO3mNsQ07YhpogUj2arG8wIG8gwupiK4VTRVV3LAF9M01ytC6Mi8qwiXkKOteJP3shoHtbjniOKkJ46rxGLoKFScQ1WnZ+uRBYJEk9gSbvcacEyTq2XJ6OtJo2jTGKvI8pZbAEAG2/dekGQmjPnFndgtjJT1qDWLqLsGrNsDWX0xTY76YlXTmstq3RDXzlXvD88lGSqVp94Dbvdc5TFNzk0IYNxEOQrvhoBEk9gapmGvI7DPU3LAm0FGW1s8ge2jLFqDGstTbsEVA9Z7AbNZNGTmpN3SRIpw4lpLLfIB7Xh9Vlt7xp0nyL6xOMV0v5/cTgWCD9n0VBzT5Crh0Z4HYNxEGa3ZoSHRJLYkzXf7I6q7AsgC+6K4ukrGOXXVaXKkZhfuTEPByjKWJmpOHvEHNtNt3nYdZJsZANyiWiYGI6DF23WfW7Nm906Z4oVNw/K7joa56gvxMDoxhnLVD1i3K4tpKtEMuB8LarnuyRTvEk1iSyhRk1scmJdPL4OC8eXXFQieFXhsk4Htdmtbf6GsVmR6K0AzLs3s+zUskoQ5vhB7jKXJE7sVsKVpxwuf9ly0k7rDHwfc72LAlMxYC62tiE6gsoD+Utlz1WTThoREk9gSOoOMFSeGVgCb59RbKJgYndYcHR+TfRbn1mNe9OVimqodA/SuhXGlmWKayHOCswCVSpEOSES43dam649kaSLwWiD7v19DCRbPhhO+WE3TeXdCNBnXA1maxFRRmJgrbH1BZ8Mls5k5nKhW6y3Zz14P9aIvE9Pk6u9XoXvOGtPUJoLSHd/PtFiaPNXoqefI0Sy6sXhf6nFCNQrPXNzVZUwyFK1QPOcmwgEkmsTUQe2WYHwI0hOzyDgTs89F5exzxKZ+U+n3JVxtZCA44Ngx71QK++D9TmZ7rSlGC17PtbpeIEHHNJG/jXWbBVaWJjtMsd4T4YrdztOfVZQgIZp+285boi9c0ZjbGMsl95yYKsiHh2sOm/n/2WBG1wulzccPZeOclibLmLiVxUxVZcnyzOmEceYFuT07Qlj37nXb7pJk3GZplGR/mBJLk7d8h7VXGpNp13TKfE9pe97s9i3TtWBk1XIyLAGkh+FE7Nm0KjkgJp0TrUBEBgjgfAisPc4GPgMAaZ2q1z1Hxe1Q4m++d/5qC4gCA9lrJoFjcy2whTCB6i1NKVFnqi6YKuvFGKqY4pzZKtV0ylmayF6LxnW2wOiud5VogX3d3zS+PQesWupTyT0npoUotwKRYqPFP/h02qynS7vTRZUFOFcnzliR6VnMPO1gAFClCqzxNFTQdV2B4LlACcnS5Cl3wcSAtexu0qaS5m4mp3AAOJebNZliwxirRYjdEBTxWE7RZN5ETWYPxIkUTbs++Bos3PZfxj2NqYcpVAb4HgK2hAAbnJ5/BjsmG1ehew7o7bQ8186IJv+1W8dZswwZ65EvpsnhnvOI8Koh3Jg5nGXQZ31sGub6Y1tBBNx7AsGtngBvgkQ595wtpim0FkYWJlI0JYe/g9b+r4x7GlMPU6gsP971cvak9TusLZXXdyIFCm9p4kRsNsYfBJ+NM/aQMlk4uHRk67EFpbJ9whEQrnIUxGbCK6Sbhic42zveFwie/04qEE3teb/L2igWXfXIAmAiRVPZH7OwQcc0OR40dodVCAdXrBG5CLgDwa0xTWT2SNu2SA4d43FNgnDPrS6NjJHhYpr4JqbTYmkqvifmnhGu20l9WdWNK85ocDyzKXKUgehvokaIJvM19J/fLIjd656zt1UCwtqwWJhM0VTihgo7rEDx9HhLW/Oc1YgUcsW8UIN7jgzgpQPno7iX+URcu7MlAlfZd97U345aJPMqyRXHNCFpZ9l3IS3cvZZEVKV8l6UpoGsOkLQ176/ThHwzUWHJAaPVnVv3+vGs/pgmq+U5vA2LhYkVTdHq0XFPY/rJu2+zdZqYrBxyN2fdXZ04L8DpnnNk9lUV05R9Bhn74GgHk30OESNjDBpnLH4pIlpUVp0iXRuOCtHUZqJMHZ4GUdrS1LaLLnYzVJwfGPkZvsre3GZ241jb91aqHtQYmUzR5Cg5LxxEMfdCac2ZLA6DZAKYtzRZ46wAUFVqN3xWazZbkKwikKzY7SnRYF2QThxD75gp95xNLLLB3WlrrvrilkCYPbDoJIRcNI3eTMrSZKSkmKabXpNB0eYNpOcaHE3Oc6wtmCSaaiSLg5F7rg7YWiMAG2803yuiuMZ9hsdF5REoSEcXj8uJOOuIK9vQk2VTonCeue4PDPekOI7paUd8P9NiaUKeWVRNZmU/Zkpr6HaUCoYuxm8V63diZ4TeZtBjXQwsEJwpbgkgLNe4gYkUTWht92MUOwljCXI1GmVjlJJZpIhqcVH5e4DtQAzYFr9tVzyf06IFcIUoRy1+tnMOBKOSwsEdg0FaO+uArdvDPEeeIqlNpHTC0SjLe68eHuDfcAJVlRwoce3GsbI01UjazgNPVZytapgYmuLlScUBkbWHosj1knMFqbsWMrt1JG0vGK4j2vBfngBNrxsQgC3+yBiETLmFirge+7WWisEIzHJdZbNoTwZqE/G4wjeMZ4Qsuw4CA5mxO5U9Nzgf/7Wn7TlE3bXR7Vucmb3jZmJFEyDzch1kJfGJqrZgLU2Okv2OIEV3DSmQ9XKIOJwioYGwmLrKOniunbkv1hd2nGeqGe+dSVT28VqfbeK1Ztg4q2TG/N2m7QUAtvinJuNxhW8YT1Vpd6yDeTbtsPMPPAdu91yJ7DnL54ZY7sPChIsm25fd/s7HsfevLgbWZZliYXbhPtGULeCsK4J+oMtYW8gMOqoKMFKuhYdXADGxQQDlBrUW2SushFQ/LtuLPY2SzPpsjT/b8DnhlTDJCoYyL1C7BVabThv9bGBfxWrGoufpdJB/xqgx/orgx13XbhZNJRsDj4vJFE2kKXPxY7+C1v6vIDl8j/kzOt94L6LjD7rmN01QwcoOc2t/Abfvej2pwGVcVNQ4QtC5yic4XZNsnabsJbxgzMYi4p+IciFpe4HKPrLOocyc6sKT8m2OQ2n1BHFg1xwa/bprvkDlYrzFZeqxuMO4HjjcX/24txL9HEfMy7P2h8BkiiZ2p0SaPqOj38dJH/xZ7H7/v3bNb6qgAsFLBDNS/dTqyiBzZgOaK6jnbhL22lkrmy8+gRZNppeD3RVm/fzs2N7vaIVfgMtmSe0MJ2RTOVyG5s1EFPd+E+O+5rApG/tltsBuONZTt62a7DnAHw9l+VzP+hcCkymaiJokwIBqtt6cKPtaWg9+nZ7btEFng6Hi7Dk4rS2ONgWe+k6cJaUmK5szO8zu7iGCxlvE90MeC/h2rYw4s9L6/hcRH/oOP7CXUeWaU8su/qq45mmjrBuTcs85rM6AbT3wuueysY5rt74Hkg7SuCXRVAesKTNtL2bHW38AccIdP8VwJQf4Hm+eh9Plumgv0HEr5vpDJ3yO+XiPaGr1iuB114k5+dxPZtFExW4wlibGaufIPsrH5rv1HSxhsvdv/jlOeddT3eM994yJ9QvRJRkcnuDsQYhwhf7Gw1EVfMT5XRZFp4gDOKtZti5P1u9wQkUTV2fE6ztVxVxnIDjzoJFB/UBvgaEXF35n7XIdUi6l3DzNW+aYWAN3dpj1Woh2O3RM0/qyrfBpCVN/VsKkywXkVwx17cUYTmROWgBu3ZQNVGbWD68rsHL3XMWuvRBrpI1iMkUTqYIn1XcaAkwWRfE9UyLD655jM8gc1qlO73qIOJnK3XMey1x7Iau6TmaPmr+z7VKfh82FtR4RLkJv7FY2NhwR4W1Mbbc0yT03ilIuqg3jiey5ClzvPuFT4nmirnvyfoeTKZpIU7zqkvihLBtFVg7nagMcLqo6LU1UzJHdQlCfyPRtGljXrGlx9ojKimsPhZjF43E3MpuJSdzh1w2T4DB0PFXrzNvqyWBpcgmfEpYmpjBu2arrY2AyRRNtaQpvUZwUKJcbkaZeECdIk5ktH5wIm+NMtu/pNJxiXkzcStLO5kaKJsBoHSm+26otTd5dLOdq3HH3HPGcuyoqF3MKzxLtyqwksw21Hm5P2XYz1vZCAPrWWvY3aAkE92wkSsRzbTc2Sk/MEp283+FEiiZr+ficEBfFSYH1baedBUQrR+jPGL2bG2gn0uLb6BSFJD0NcqkXl10IudxCpSxNVQYWLyBaHX3fs+PsGV4A+12WyPYJaPH23DOq3IWjbEfTKCMcAGRV2gnLu69y/+i10xvn5x7bGfHb3dBzL8Bq/COYTNEUxb5UePJlLkBnUbjqy5AmWo+/vcigpIUDZzmjLE2ukgO5iOUCwdnPAciXsNnSZI+v4r7LEjvjfJEPSESMdg0Ps8D2+isarKmTGEtSN6UrVhcV8KsTsmlrPnuetgkHsGxmNp23jJUtmUUaxfb1YMJ+h5MpmkBaAHp1lybt5pQlOfj36Hzz5lLnYJvWumKHaGuOJ+POa23xiibDmLjFu/+cVcTNc9owjovPsmxKfMHdBktT3EEat52uiPDc9+ZinRt27XZrqmKaRuOtnbThHLTL1FFyAFvPMY1b2dpNlCgByrm7M7G4aLQ8yz1XG54AsqYtEnv/4pk46X2vKHUONobGW1+GqaHkKwVQQjh4XGFEmQauRpUvew7wxTRZx6UdY/VwSjTlfQlHHZv2z10qe66CxdvZ77K49oqq6xclB5x91RpBEZxdVjQRJVscVnpg6zn21yPSYkSUERn+ubb3QBjV+DkmVzQ5Xs6TpmjLEuULYomifaxvO9th8EUkfZaTeoQDY95m23nwoqzemCbrOMY9B7AZcdYkBG/l85KxK9vgXXNcMU0uQTpZL6xayZNUyliaOovmsBBvpwNgG0tT8TsiXXR5CIw3c7CzaFoDXQk6Y2ZyRZPjhroWMNKsGSSOzu85RSyQ+cHnREY2hoxpGrmjHhbvUSIYuqKYpv75q69rlY3xiibLS9hqjmeCu8l2Sc705SoTRTy98ABDMO0wRrqT+s+FMoptpJ3FUt9R9nwb186O7Rk68fwAthS/ZX7b7Lp84ueaN1vpeql3VN1Mrmhy3FDPDyek4FAvpR76DrdTcbnnyLYoJsvAQKxHPq9sTE3uuYpSv12tasq656zunh0us0CLJmdQ6UgRvr6C+c/8Py6rjNvS5GrhY3DPFb3tSsSsNIi0bbcUbTmeSKaI2czjEfWUimfYcQ1lgrTT9gLiHXbXh8JEiybW/+v5AUzyTqxb4oHJ6VuaqnE3FWMc8UlUHFRtgeCOZtLMyziZQRq3qMXVG+hMWYU6i1nQ+Pqq8ZyGe5f0iqWakxD4ZsbZ57SRxp0tr3P2y3+Fhc/+FuLbfpM+tXv9SGaQRgn527MLrZ0Icm4CZbO7OPccb9UaJX7LFX1d8FtKrT3lJvB3OLmiydG0tWmiyZtmv/EcpKUpN2czPmryZeepVusWTcYA5/7neEo0EHMqCogSQthRpBMYsHYYYxOA0d8vdR/IYqllar5sbynrWS2XHqDP6372HIVi2cbJ2fwm52U1Djwusw3jmWKuHvfciA1RKfdciWu3Ws0msVvHY8XA0QAAIABJREFU5IqmutxzE3QzT6TfO61Efao4yeLHzJamXvNTqo4Ql0GRdngxWMrStHbcHtsWd7I0XyYOh56Tb0fqdc/1XYFbC2Hr4szHfBGiqURLhu2+n35wP/8clY+HqcaaqpgmG90S1haAe1b7aw3RpLmzvSeADa/YNJ8S7rmqatWNmwkWTfyPuamWprjETik/z5YP3aay+M5Kxt2Vka6dTZ/BvMQcffE2fJb1ZdyzEJhM0/CJf8bk3/8cT/2sre5ltM2x28+Lqb2UH1+Z1e6EsVu5e9POruwPy9Mkmvi+h02kvKWpF9tjaXhebAaJe16IpsNbfn52Tv532C0Rz2X93vqibnJ+h5Mrmgo3kL3OSJTymXBldhnjpm9pKncN3ZEuqsECew4rULFYGB9QR1yPqy8efFYGqtGtNwas4tisbAxXcsByrM/SZPx+vDFN2N6NUljctngxDR2zA61ZaPfcCKvDhmMDbB0TDOlglmHJmCZXhwBmrdn+uetvMB0xTR0+E7r/ufOmopqlimiOickVTe1FREj5RZIsNjdJCvhE+gJmJyxNRGAzWNcZV9agX3HW/hID+PikbG51NE5domLA0s6uLXeWIz9n49+OHAMYF3yrQE/Iyt0VVFTesgn0VsG0cTsbR1iayrysNszJ44I2/DZcfQ8bRS/L0FhvaCuYUAI26QZAbwPZ3nIDWS4Q3H/txbWMEEOKaaqR3GTOCoI6gsdDoUy66YbzEE14PQ9Bl7U0wbeY+aw6vvgpqtEtWackux87aGmKNrvbAAxkcDGFKE/8jC0ESlXuue6qcWN0QkkKS2KJy9JUzrVD/V7jxB4jN4GxJOOgbPFFylrrWAf7FvQtfpuOVk2D8/Feu93yPHm/wwkWTT5BwIumyW3y63oIh52HDGbMPtPjViBFE10IrloX1eAY8/Guejz1XDvj0tzuvqcnChSizALVhqJsevWocZQ7OO93WaKatCOwvdtZNFqaJu9lNQ5yj4anPU8+HrCtbV7r5KgNpLt+WXvBfe1Wy/MkZnFOsGjqWZpo0cQGj0/OzdxEnADYCUsTUWvEUcnYcy89Red8ffEc7jmiHEbXkQnYdYo/b4sRruWJTWBVkV3ocm/kYy2u29oDwR332VrBOopdZVuaRr6h8ybTUO455yZ3eAmS8nFZZa6dtzRNzu9wckWT0/XEi6bJ34mVvQa2Vk72mZzlBABXsJFwGQ7Ozd+0lnTPEbVZAFscyuCYaOUIFwflKFOQjbO5AruEG5P7fuzBqH0BysV7ZXPaNfL35OkOUGf2XDbGZmkCgK4jNq5plI258SVTkOtaZ7vfbuRaK7P5+Ov8ma+lhPtwXEyuaKJimvovl0aKprKWJqK8v0fMenZY3faumtxz/ELGFbTzWNl6cVB0LSzPbtP4Ys1LCZjcEPOmFgvZsbvMAtFrfc7GLmZz2uFek6XWj/YCL46JWD9P6Yqm4Qkd2DCecM/1Nx5sCZLtRZG36KtnQzf4mYA9a3CS3rMTK5q8LUL4mKbJMRtuxY5YmtaWYKo14okBcsU0eSxNHvecp0GuPVC7b2XjLE3ZnEihZUgB3vxZu2zmecLdkwkhozVkZpdZIBbuBM8iX2zCdnbxLnO+bmexlyTAiONF8/WnxvinJuO1/hTjiXAFr/dkVIxjGSuzZz4At6Z7soHHycSKJn/2HGtpmvydWOlAcCbuJpk1Z1wV53dZc7wFHmtwzxE1xDy/Y7pEAwauw1VI0+rusS3O6cxoV1j/83PrkSG4uaSlyTt2O8qlq/euZ5l03RJWTo/AbBJM7auh45n1oz3fK8DLfVZ3hMXQb2Uu456zb56727oXw2OCRZPT0lRxDFSIlC1uSfm2rRlXg2nucavXqoXNnuPSYVOHu8PTty3t7O7VECOCPx2WJquLKxvj3MUSC1q2OFt2lnYhxlS1L+NO6BLizEb2GysV09SbExOIS323sjSNpGytO8pak6+dLkvTNtlzhJA+8byAcxPCxDhO2O9wYkUTklm+KjT4RXGSK4LnlLc0camwnp0NG5TqSQVOO7mrhwvoTdvzvhgtg4XAmzkIbHVfh9dcSju7e5/D/f67RGBxtngT7jkqTqkmS9OoZ4XoC5adbwcsTWySAGFpmqQd/jgo3VUhbiFtzVaS+NAfs31iiN/SVK6Eh3Ws3HN1Maqo11bDaNE0OTdzK3bO0mR1qVTfG62/C2deKJlwiFcOcXPr7CZfXHaBkrYXMpM8dX5+IfdaYYoXq0XgDE193ownTomrc1MipmnI2GhMiSSM+C7GtBcRrS+bCnx2HfW+msZOdFXgso89RXu330B6+rTm5wWcLus4ycSiMaaJNX6Mk8kVTcgXdOLFESX0CzMijw+RnbM02eNQ2FgJdrdRTjhw3wdrBesyAiWK6B1/fzFj5uSzNKV5MLLBOmetVM5YUPrzNjyHUewWAtYXhCscgOiPOUhx7czGwLPDd1a7bgKe3pObz8HUuVuk6yKNqqeUthd7DdG5FmKeXnibPtfUgkmWptqw7myL40kLSGYBsB0/d9f/i4Xb/ov53HVi7bK9FWy9jswyw1pzuN2QRzj0A2vJuc3s4nb7M5xAoX+XHUfgvNN11bfO2dxjNuuaPcuNFbre+IjCcjliLC2ajLFtw+fEi3wmNisTxF3aXd0oiqzQeoqUliqLssXvhNrEDRLFrkK6g/OiYhwnRLxPtmhid+ism2Vmt3mnuPiJX8f8F/7AfO662RE3gfG79hTNS9ucdcqT2dElxYx7bmSxzswyZxdynsrXfVHAilkuddgkhIj7wMb1eE39VvelZ9fNCO6Nc+rdM0LkM1Zhq1BsOl1Hy6ZBmEbhHlfaqAzc4nkjN4vZfPy1vKzvgW5nd69npL3/5jiZcNHEZRpkIotZgHZlO8UJ8rduhe0atgok5AICWcsMwC0s2fH+rLNNY0bscLIUeUZs5y9622+NtjR5SjTM8Onr2dy4QGyrhSM755D5pycem12rXYD6FnlrPBR1n5KZ3hifi99Vj4uwKFZVZmHa8MYE5XQZ95yrdc72QpmxFm8e68u8A+zvgUn7HU60aGKrQrP1IDzZK6FCCZITMrC6ZEBg2tntsGiw2XMeF5Vv8fCIbaBCl1LezZ5yZ/IB5/ncAKto2t0LQt5+x9ht5zvj7c7Z+w0mM1nJB8LS5Hpe4yRzRYyMaXK4g73rR9Khrh3oPxe2Eg0+y2vTKCMcgNxjQWyg6E4H26/PZX6HacdeLHXz2N2IVx4yHOezgo+LiRZN9A59Zhdl6nYFz66v2o+tmoFdeykV355HGsVc9tzaMeq7YBemfuyGpzEwmXbfyd20A1/oNtapvkA5ZDre0xImcxkQ1xHFrngfJkama7RmsVYvqlZUidYglu/H8xtl1pzNc9pNFre0PxeTtsMfF2XbzaSd3ebfQNrxWOm3t0hyyRQbN8zeZr9ATyxWVHZlnDRLNBE1Z4BBN4t9TKgpvKV+kHmGF/HgA6QViEiVzo8HyJ1/bp1iA8G3zCAbXhOpL1BOvP6taijxYsYb++ApbgmwMTLbf79sLE1W8Xibcw4I0kyA+mOIRl0n1VTaGUM3CBtP00/asMSSWCx+wmM53zB+xp6l2J05Kct0I/pKjmqW7XXNA/XENJUpSjsOJls05ZVQjZlhmZnUHqVfBPQSL9n/v703j7Lkqu88vxHx9j33rbJWVZXWKgkJqYWWakpUCZDKEhK4MYvnqMX4mMNYg+mZOSM8+LgZDo19GHqwx+MRVrcP7VFjH/BItqU+YyONkRDdWCymSwIEAlRSCdVeuVSu70XEnT9uRLz3Ml+8+P1u5Hv5MvN+/kFkxY248W7ce3/7NZajzZHrQdzSCZyFI9BsWFY9ZmB3wj+uhSE4mJZSSrpKfSdO2QVVYUbFBaoaCE7LdKONO7/uF9fSpJrtU4is+6UWX6S+GfBLcdCTBLSliYa0mKivn0GgM6UumULQdpTQEcf9xTnyqNVz5X7bfn/eaG7ijS00pQpeSi+tKrRIFVlR+iqaYq9mosRxEQAy84xsaUrzs3LYliPDUPK3KwWpK2hqnM1O1maZZx2mGygADFTifTgCDtkyayVl4Tuy5ZJX50Y1fZkinKnFRMZxz3EzN+kFPjdTzGYncRVKqDTi7yMUoUVJwElkZfhEB2Ka4ry7SJdl0c3l9nPG3WDC+4YXmgD6x8A9X4pVhM/fXGIKJ2uNMBMA1sDSxApmZPjQgzYqh9CquLXULDSyb8zMS+Z3Bk58loL2q1TDiCHgcGIn5FEq1IwiptWOebxO0JagVbOsPomsLKirWHIAUBizIEmAXkF9I1VjXg84pWdatmdY3jnXBnjhE6HrmpmQ34RKyQF/nVF492Bdi7I8bzDhfYMLTdwigtxYCka6dZovKHQDkSzIoOSYbkNuMCPAt8wATOtUuqxkNVIJBAf45Q04liYAAOPbcVN8ocll1inzoQo4gRuTMCacWB2RpgfK16sjK2jVyehvg/X7BdXe47rnmEkCaWK2p0J23lYkOIBbOYuMrnS56TL52qZnpCtt1wOXofQ237esXHbHJQtN9BCAXmBDC00u0//Llmg9TZFl6u4xS5MMSo5fpp5j2VA5MV7Fly9dhjxhUMlFpVSZmS6giKS/uPAERko6b1MbxSwglyi0cBInON8TTwCNd2hvZCA493tT3Kx8lArFpspka+pWOLS3+PWHkP2vjyq35+4zq9v7ghB9H+GGU0SFT8iYVJWSAzEKY1J/N2ZZkfVmQwtNwvsYqQsZ1wIiosyerfrSgwMv0nQrURgc37aK1a1uzaFvSiqCg9ompBIIThdQqKn6TfdPl2DYS6wquiqB4H47zvEoNPcc/YwtkaRnH6lkVQZtI+KhhGGxhXTy9xb2TIXz4USmDGOJ1k9X8diZjUTmp/8PCs//nnL7OBW1AZ4gFLp2Rgy/9ASEj7lKLCfQeIqCyroh24LwLcYt69BNNoXQRF3IuJWaAZDrZgTBbD2YPScUXDmr7+Gfdh/t266bWxnxSZmKbENc7AHfPaeQQdalQHBOHA4AlqWpnqGoImjxaomJZIE2ln52IvXcM07JAdcGnOjsIxUrZ71PEfFQGbXvTfUYlaA983y4UAWnxca70Q5LXQ9UlKam9gzBI7BKhX5nIWVLIi1NvAK99fuGGyaMCEnOf+8o9xzgKxc95qUJYUMLTayiXU3Xc49eISwqhuXdu/cWIOnPjhnTFPi2Ce8XmFtVqmh3wT3HjHdpWayS8hxigU/fOgLGu3AyclY+h19yIaJOUlO/iEcnEAvfyefThdbgd2EI3/Xn+MJ+SNtMhW2xjV3jRyGbSqTLbawOK4oXMrPztiJxLU2sEixB8gDXPVduu8YrxzOS3r21IBcoMEu0rEFtaeoCwQJJds/5iyJnY+ZpYnHdYJ1ALqLxSw4AvPgxloZtJqSVgiM4pEpeqr7NaFPkW1sUqmlzzg0TGU+7XORZ2QCeZVM1NoMn4NCEBKpFyn8+QKwVpfC7BH1KV0LaSo1aZMry3ziuMoXEg6b2KlmlDGVCWmt7zzreS3CV81Ukc6tjY8O+IcNQKycSscarWNiBeO9OzZ6T12r3XHewUhCJbOcCweG5tjgVwXvQxBi3OJt/D4BX3oE/8XmxV4IRYBm0UXTfSI2cFwhOfY5vksfSNP/+SkXwuNlY9I2VWjm+XmiW4O4NBKHo36cu+NB/y/pzPBdx2LtmvGrNBDdh0B9FDT/oU2Bl4wjHZXKauLyW/1ttJbjK+Sr82NhV8yLshAAFC3q65Fm2W5+oIKgZlS3uCyi+eyIjD60mtKWeU9cLbGyhCbwFXdaryHYoNkVqDj2XPYf4xdmABr8+WYNVOLSXqfW6cawtSgcKcwQ6//cimuStdJPQFB0v4McacIQmtdgMkS7LoG032jpHPjohXSG7e11OwkciA2GmlBb5wOIX5trLRAhVre7JPIVgJa7SM+lp4u1deZsMxTFYk8ru6TI9jIRxbdBmlRLV/K7Uw7RXEjcrXKSKAME956YrG8biGSk0Pfzww7j55ptx9913d6M/bLiTnitAcM5cA3pTaJICzCVWxelW9wCY7jluPSRmjJKKFqRaE4TjopLX8wQ6N12B0dLSFB74CXCzDdXcc25ggaFakBgBrwTrmm8Bolwr3RtqLqcoITwQqliCqgzkVj/0NMxl2O6ZjGKK6Yp0VzPOOtuoKJ8LaibgJvMxj1LhFbvlr09+9nZzH4V3AG/dNc8U/PywCUWl26VanjMVmAxL+3oSKTTdd999ePRR9RoXnYbkemrQMNhCFlNT7MWgymADj3GYMLfAo9Jp3ekKW6MGmJtYsAGrpI4zYuEy/nOmiP0q89xzCu+u6rriCIDUejCCYUHhWhTdjJr1JLKEiaLQBKi77TnCZdAmQ/+9/Gu3grWJO+cbiRviwAk9UKr2HyXwxzp/Tr1kjUiXaDFN6bLnXuRZwtaDRNQFb33rW/HGG290oy9kphdr+Nk5qbld7+aQnj+L774evqjsnF7EFQAAAyJTid40moSsCgzhwKjN1QPb2tCLJka3ycQ6pnQPrlXHTZeR4BZfZC4Wde2JIQRnVAWHCsypnzGu9zcj2nNEpkyqZxKQyEKYCZ57LijroCY0UdrRs+focUrsemyKliaRKnrV80P6pOIqaywNUWB3if3ujc80qzOIsi03bbb5EX4HNxDym9im1JYUc2O0tgrL9kWYs6+RnuWmS0hw64FFWJHjHIrLSSxY/dziinWttfGhrmTOQOSHlZ7VLSKFJhUMA6hUcp24NQDg4ae+j6dfPgsA+ELSwQ3GeXz0q8dDr/+IdQr/SxLI5NOwCgMwZl5v37+UrImSy6YhUn0AgHJqGaiELypWol5yoFLOtp1A3cJMJ2CYBnJ9QwCAUqoKyzJD392YzwAACoU0xMpr3BQAIGsuIt3wb5ZlwkxZq+5plodgLk2v+i3MTBIAUCnngES6uU1pAGZ1pvleVhYAkMulkFnZJ1OOR95aRK6xTwkTcEPe05RCY8FcgOv9u5GX/SiWskDIb2OWh2C+Nt10z0wmiVTYd5Qfl/02FpGp5GCl5FiE/fZWvh/G3Kng343ZNmMRdKCMNBaQ9P7dNA2kUonwb1vIdOassYB0JVcfi0oOMMKNzsbQqHwlS86LbKux8DDLgzDcKip5A0hm5bxwxeo+1eQ9C9Zi0/sZOfmdlVaMhUgVkMF86O/dOBZWvh/G/Nnw3zplhY9FpoyMaH6OPxZGVi7s+cRS0/fWCn8sEoPyGy0ll8LHEWgeixXId59b8e71eZFd2WbJ/22bn2klTMBsnqtG/zCpf71Ou3XNh/qOrcbCzPfBdBdaP8OR30cul149Fn7/iv0wLrwUtLcSJpBs3Wez2A+zNtf8/MZ5UWzxjJocx0LCm0+23I+ymSTSlRyMAfnvxUT4b2AlLRjm6j6ZuT6Y7vyqvxvnvXWzmAm/Z6EPxoVX6uuaaSCVTq6+l7dHldPLoWtwr9ARoUkIYHqaf2Amlf/58B6894BcGK54cTvG3jiO/+veA6HXL//nbwLnganpBSSsIpILF9v2z1hcxCCAhcUqXGsQZQBz507BxmBom5LtIA3AEA6mz50HUnnFt1s7Css20q7AnJ1GBcD8hTPIj7qh7564tIQ+AHNzy6i1uGYgmcfyzAXMN/xbn+PCrjq4tOL6LPIoOMuYPn8RSGbrf1+qoQBgemYBsJr14JzIIV+dw/TFWcA7aNicX8QAgIWFKpZW9qmaxhCApanzWGz4t7LtwnBC3tOWbRanzgZt0vPLKAG4NLsIx2z92+SMAvLLs0HfBgEsLdWwEPYdCYFBK43l6bOYn15AoWoj5YrQ375oFZBenA7+PTm3hArCxwIA+pJF2JcuBL99vytQq9qrxqKRgXQZ1ZlzmJteQG6phjy8udpGaLKqGfRD/mYFAIutxsIjI3IoApg98ybcwhjKtgO4LmZWXG9W0xgAsHjxXNO90gtVlADMzi7CNep/70+VUJs93/LdVo5F0SogufCT8N+66iAZMhb9qfKq5/hj4aaKMAEsTp0Lff/gPo6LWtXGop1FH4D586dRLYW3aRqLln260NQnc7Y+L5ZX/bYp+W9T55r+rWy7gOk0jUXCzsj+XTiLarFza3anqVRyoeM95P3v/IUzqFai3zG3VEMOzWNRMvMw58+2fIZ1aQn9ABYWlleNhU8eOWSWZoP2FduFMFfPCwDIYfU6mFmoynk1uwjXWd2mPp/Oy2/TWZbr3FINi9MLsGpyDi9cOIPl/pB5UXOQcFevmyWrAHPuzVV/T80vowzg0qUl2CHvXTALyDSsawOuQHXZxtyK65NOTq53507DTky2vFenGRqK9iQBHRKaOk05m8T1k1Lry705hMSrl3D9tlLowv+jbP013TQv4ExkGEGoHmZ1Bm4PCE0+KrE/Le/DiOupx6xMwW0Qmtq2aSghIDJ90Q2C+ieM9/Iy1ahuM596PNAsRLY/uoFhsDI73XQZIMY/+ahURHczYQHnbdowXI1NcVOFcFewy3DPAd67Et2XcWoPtR0zdvyPEasEgo98d8aaxXDprUX/Ngpx4rZEusRyz7ds7yfjmFb7axtcaaR1EICb8tfO1u/oevdRK8VRgnnhZXY72bYCLF6UlpR27ksFN/R6seFLDrDSa4XczDkBZ0HmEGPR6rW4prgHTvpwguhdTsZT0IZZQsArBMedaCqCQz0eiC7YyMB2hlCwzMtwVKk2zelTY98AYiC4tzhH/k5M4dXN0FOSOXWKViIybZIRmHXh/PsB/DiyRtx2fWr1zFTBi82ijBf/+KKNhkppkpXEiesBGoWWtU98AOCV2kiGxjj6mZ8q32GckjVupg+GU408Bkgl4WG9iBSaPvGJT+D9738/Xn31Vdx+++346le/2o1+keFn1vCyp4RC4DBnY+0G3BpLoffhFDn0N0/G76aibbDqdAXP4QsOKhq5PDiVLjQBvCwrlSwbFYERVhJuMk+zXPibA+EZ4WUWViMFduq1Xg0oBSUh6ntil8VIFmTAfow1gWtpgmGSlYl6oPrmFZpEUlr9Ywk9Ka92koIgDtSzdinfASfxIsCvJB72zVtpiESOnM3b3J9SLCUEiH7vumei94WmSPfcF77whW70Qxl2/aB0fXAcQpS+y9AUhWHJTLueE5oisoKIuJk+WNQMEMbm6dN6LKOLPKrUHVK3NHEsZxWYc2+SrwXk70U2yStk2XCzAOvtaBs3a3NgWFDcdBkJqlWqQZHyx41KlDWVWxZDbmaEjN02uJmKwjgT54VpebV0en+zUke6hejjtnrNEZk+WW9reZb9TQGN62H0vOBc29QuSuDP9CkJJY3njvoCHblPjftAcSL8Gb7wvhksTb1O3YpCWNAh+GZAX0Kn3J/qmug2prUmxcPcTB+j7hBfc2hrNQxxhyu5qDJxLE0M9xzjN1dKLffj8xiVjlUsc/6zaHV/VsZOhPdNMI7w4AgrceIjgj6F/KZds+6t6BP/e2VYhbdIVXDWd78i/sbNqgkyPpy9wY+Z5O4jItXeuqj6HaoKcQBDiTIt5bWp22x4oSn8kM3WqJgBqR+binWlW0iB52Kse4hMH8zFKdImzS3uCNQtTSz3XKaioJF1y9LEDAQHt+ZUPwy3yqo2HVh3mNXhyQUjvfOmmhbJEIHXTZfJsTRB5WpCLKJgFHds1SfDtYFa62wgV+lcsEosRcpNV+QRGI1xIRFzMGoDbb7/xtis4hLH9SPSvtCjtoZyArFdReXbzfS1vb/qdygyakKc3yeAXo9tI7jnNrzQRIqdaVi0VQKUqTEwIpGBSGR6z9IE+eHH7Zeb6ZMHloZsKE0ksvIMMKY7C+DGDfWtfq+oDUXB0iRSJeni5H43tTnAiT6zTcnSpKABBhXRVc4FJB5jQlUyBCe4O+pcuMZrYwT+Ro2DyPLnUdRmFt2ndkU1W0uknGduGUtTjGD3uiCjNo4sS1Ow1vAENKnUhrdR/Q6DEJU29w7vE91dv1HOn9vwQlMgyRIG1M+eAzpjafL70ztCU1144LjWQu/G0YD8zZMzSf2MKsbklAtBK8tJu/RWhfO2TMsLru1MNqCKdcQNzPic34ufBQh48VnEY3ioGq3Lcs/Ryx5wq7Gv7BMQLri7mX62tSGua1xwQwrgCXfEecQp57Ax8Q9TX4PsN1X3XKooY14p7U1LCrLc0iDZ/rb3V/0OXc/SxAlNCNoyss9V3nk92PBCE5JZiESWvAnIj9fkZcAwLBMi3ddb7jnPNy8tMvHcc4G21TR52sWsMCepYcDNDvDS+jP9MkBT4Vw4rnYtmG69Tp6vBqgt5KzDb5va0QNAqRqttMTNkyxxnN+H67Jf2SdgxZiJFcqHvQjU2qdQr+xP3EDwVX2KbOONAcENu3XcczFKDsSNVzUMT2ihtVdRckWm37Nst3ZhBwkJjBhIeV/v3RcV3j2RgUjSsvY4ZUXWk40vNCFa+2uyORgmW6LlxMDIDIVesTTVWQsLWKg7KKRomYo52M30s8zSgbWFYZ1SFRxcpluPc74aEjzhH2iINWBZ5tRSe/3fjNYvmrDMssRx3Je+xVIpfqNdLJ6hFKQrMr5wWGX3B1C0NDGUicDFzdxMNxpSYFAtGVDyLEXqiidnDW4ZdhB5//bfpgyvsNkHt9ffXXH/yNKy9nRMUxdxs/0k91xwPVPzCyxN7RYV79/Wwg3WCaSGvESLRwq9B2+TVjEHc9wKjX1iCVqqggMz+4RdGDXHc/2oBIwKRSuM/5tR+0UrT8CoNN7GqmastHYaBlzmdxQ8J8LlWd+Y+OOkaoGuHzLNt0JSfgOVhIKNiCwZoGjJMEyWpagVgjgvALV9JPhOQsZcJTlHNvDfXVFgJL5LkKHb48L7phCaBDPOgLuZ17NXomJgDCUNoRsEtX8UNhIfrjtIxS3BzfITKpYmhaNxADVhG2DEAuQGYSxeoN8/XfYCRunABpNeAAAgAElEQVTfm6v67pSjY4J+EZSMhnuSFtTsAADADP19VqSIZ/pZv2W9T/5z2gtNrG9UofxGy/YKFlhKP/13Vvm9NgrCkofLhn8/0cS11rvpzlqaoqyggqvENeDGSCQSuX6yYmS4NozqJaXndItNITS5EVkDK5GBjzzzOrAylie8Lwazdk43CDa9hTiaEi+IWEUzczNqlibWhsIowNgIW9gOLHPE3ys3wFvUg4BRjqXJrzbMXZAHyNe6mRYp8m3uSXlnWVk7Rf59RHZATTO20nCThVABQkVIVxVUg2emiqx3B3iuW844bFTqbnz1dxQxvQgyXIEe06TqngvrY6zz5+K8O7EszEYR3jeH0BSRNeDjyzHcYGNOmQJZOdZRPqunUwTxSDEsTTATsk4No8ClzFKjB82KbL8sVkkIDgYUNX/liruVkEy9kOeky/IIDeoikBtgu5So336AmfDqI/EWJjdDF5rqQbPt54t/T9LvE7jcaP3mCt+NyLICHbA0xQgiZocgMJQJlSzMjUYwbnEsTQxLUSs4SqTI9MOwF8hnpALRY65yfmb93uqJRHI+EfbODfIdbgqhSWS4G+0AzMXz9PszNDGqCyv52j9g6I+3wYpxcjYHf0IhphTPMRv7VXRZAmqEtrSKJD/oVyTzXmkD+jcAAG520DvTjKipGQbczAAM4nPYliaoZUW62UEYC0yhiWNpStNiJ7jav5sdIG96nGtXtW0jcPkuUW5ZDCDeSQFS0aO/D8s9FwgUvb1ZxSFYw+MEcmfjFQgO4koJSqRSvGKEUBQnti5WrG62j5S1F7jGF3jrcrfZFEITJzYCANzcgJc2TAuKdrODAEDaZKnSfPrE0wCA5MnnSH2ISzBhYrjn5H0q5OzA+u9WX2iMyMKTCu62bD8MTjqsYcDNDvLr7fjvw5jUrMD23KDULhmWOZltyHe1cS1NnPO2yBpjItPWFbb6vnShUmTbp1+3o63AFRxJxFAEfLcD8zdvRGQHeZsJo+bZlnDPKWSarkQqKOqhF5xTBZSCtq2UPEcwVOD3i1Tyx1muY4oZlllarFKwX8SYJ91gcwhNxI1WeFk2HCFIXs+3NEUtqmvhY+cQpIzH1CZFpo8soPi/s7FwrsW/hpQpUCrYyC846OYGYTA1Gjfnfwf0dm52kHy9CO7P0S5VLE0DMJmWJhj0pYKlZDAEIc44111/ClWMI4RK9pFEyZwsJ8H9zRuf2cZl2BLfnUkJW/Atrz2+WcVBRMSqUXAzfaRYvXbtAWLig6J1sm1SlJVUcs0D0jVJPg1iZZ9y3j4Q8dsHxo8et3huCqGp/oERU+GDRZ2otfqxKYRNth4v074vroLVIhZWEm6qFNs9x8mi4Aib9Tb8icPNngR8F4xaXA/3fegxTXxNSylglCHINbVL5on3ZygZDGGA555r9x1FZ/WZixdDtWqhEC+l+pvX2/PdjeSaZzFKNGwkBGcutmof083aiTCPVu3azSc3N6S058R69zxRiUpkIRK5nrd4bgqhKWqjNVadWM208niVqimxKW5uSN474sOsZ7fQP2Bj4Tzv6I9Vz6RbicLgCAG+hsGKH1M4HJIbJOv3jR3TFGhMPEsT2SqR42eP1OtvMVx62QG5GDMP7aVm0Il0xVMyWlkYV/ZlpdupTYX57IAsEkmYA/UYlpDfMqQgKyCFY8NZDj+0V9GyGW/D9t+dkVTBEO5k7F1vb1ZxiZM277cH1L0DbtbfG6LnharLVCaGhLv/qPtYq/sCajFhIj8s21L2zywv3ng92BRCE3Wj9ZdjFSuP1BSJqdFWOnJiCFP+9JwPZPDPrkX5bz9Ivn4lbrYfmI+esO3v4cXdEMy0wW/BETKConyc9GpFF9XiBZaPXqQrEIbJ61t2AGb1EgxCNei6kMm5v0oM2AAMCHbl+iCZIAqOkpHtbx3r00Ko4VR/j7PB1TeI1m1VAoKlcKg+9+quYWasH9n6rliiYQMRJzkAaFSI1cbRZSiRbnZAJhwwnxUc2huyrrlZNUtTrOzDPM2QAGBDWDw3hdDEzTRQCcwUVPO6YUgTKFFQ4H7AqTf/kXV9I25uGEZcoSnnaQ2UyeyfJceZaH4wIzemaXkGcG16m+wgDGeZVwXZtKT2zhK2IywejShtjPyF3HdPc61zrAy67BBNycgOtF3kWz2f8luquIYb+9SurbTg8IJiY2/YCq5hVqYrs9bdemHOn0bff3w7zNmT7LYqCRCNBDGaipYQkSrJeluUuWom5Phx4y4jwg5khq6C0KTgNQhgCYu8Ar/rwaYQmupZA60HZdUxC8mc9J0yAjNZNWIYmS6qE1AFNzsIzJ+NdQ+uy02mt/PeUTDjjepuM46wobYAcs3HgYBO+dYyZQjDUnp3jtCkEtAONLjnRLRwKnIDNPdcZgCGWyNVAfYFB8qiKjLSKqiSJh2VaeVmB+SxI4zKxSI7KDdsxbPPVAr/BcoE5UDkmEJdt0j/9Akkpl5B9vi/Z7d1I6wwke1jWpqkQk0PC5DKN+9Zbm4Ihr0AI8y1nB30vgneOYgqylmAlYSbrtBigmMKtt1gcwhN8APcIlxiDRlb7Te/1ZOKIwhR+uJjdvG8Jzc3BCxcYMeyrLoHAHOBJnypxHJw26gsZkIxe5Hqpq1fz7A0GaYsascJBPctf4zFNbBasGs1+aU9CCnTxN/JZcRxseI8DNNzVagfpRLWJ5a1NWgzKNOuFc8+Y31HwTObv41VymMDMl5MrURDN1FREoK22QG+dbmRZA5uMh/TzUrfGzjX1tu0/zYD4Yc7L1L5WO9OFRbdbL9cl3rsRI1GNpnQRLeikNxGRrOQRY3lcXODXbUgUXFzQ/LQyjhuAmY8GNmt2fiM3BBMhkWsvlBwxl8te9Hlng+X5QkoXJ++mqVJrR4Kx0UUxPBEFbRj3ZNXa0clQxKIFs66+b2t6hPHOt5SmYgo9dHjLrq6xYNvMV+LYzo4oRdhfaBngDJrcyFagaxbmVXmxaDS7w7QPQ7S8lyFUZtTek432ERC00joRttqmeBvfvQg3SCeg2qKZ2Q+ASBXPl9JbPMyGhd/orakEHDt5kZ4GxLT+iX7pSg4MGO0goXapZnD2anpVlrWXmG8u0pAu+wbT2iSWn17JYN1/hzzWBqOK6TpOasqxjd/u7R5tKINszbcqrulihBmkmdpytMtYnH7p0rhGw9j4NGr6Q3MhPwfhTVMrME7CgXrTyMyELuTlqb2a6EvdCpl0OWHY1iaaK7GjXD+3CYSmggfWMM6xo5N4QSz5Qbl+XPEeAruJFad9MGEYvrJm7CSXmwAI6bJWWZpDm5uSJ6KTXQVqPjbA82a6aIS2UGY1VkYgnj+XKoEYSbp/coPs6xsgP/tM74J0wrqrnAIitRR4o+Cb619v1ibtWFKTZ286YzAnD9Durb5OQbcfAvB3bM814WRiHFqtFQrlJNY1afsAOv4m8AiRvgN6tfGi3nkkv3hn8u5zkTJ0pQfkW1Vvgn/HjGzIEUQekBIfPDik1CluxOjrKBtjyqJsgor1ngC6EVsN0J1+s0lNNXmyBVLBdMCEq1l1+8jmJs4dxIqm0jXwNIE+NoSNabJD4RmCKjBpkRsk8zCTRV5v0si0zZ5ILRvjAwyAMFmR75/bth7D45lTkEj9YQVDtVtt2Hx6l/H3G3/mnB/2jlSdfci8XvKj8JaOE281hOaFOIj3Fy48CrrUCWZlk2aEBnZJ5YFlm4VDgSKBXWBIg5GleeOMVmxYXL8HQW36krkGMSLaTLcGim2TUXJFZk+mUwS0sfoRJ429csY6/6qtrkhUgB6/TvsrvDOYfMITcwf280Nex8vTcshuaV8TTRHiF9oWMjZliZVE2mMeICm+zA0DhWzP0dDbu4TV3CgZXg1t+ELG/63Sbt2VFrmluibgpsbhsEcU39BZmElMXfos3BL2yMvFd79I92SVkqOA9HC0U6YaXWt4SzDqM6Srm9qmx8OFyD8siKcxINMn1LdneY+jcDiWEmstJe1RBCa/A06hhVGBWFY3XmuIRVlYVjxLE25Qc8Kzss+C9ozYtuU4uBMy7OGhQj8CrXzgv4wPQBNbX0lKmIuOmtgDew0m0doIlhRRIP27uZ4g8OJm+HG2PCFJkWhJ5WHSOajJ2GbasmAt3CQY5oU4o2UspN4WjjQYIlgtRlmXS/bjLLvz43pYgt/DEFOBZ51jT4OnDELfksFl5PTJkYS8H9zxn1NSx7jEePYJOkyZH6vuTbCXyNWSgbOd9k9V3eZ0ayHTXAtiKYl164Y7xjXYhjsDQRBXijEavrPCF0P/MPKVQpcBv1RyUilKWlrIdh2mk0kNPGsE2yJNpGRWttc9OTmWleoG54fGxPLvZZXN7H6sNJmC1Jg4FqNAL6gxRccRmExF2sVYYMlNKlY2bKDMGvzkUHXqn1Swc0ONpX4aN8X4sYOT3BYukjS9OPEsLj5EbmxhSRpcCxe9TZ8oWdVnxYvsKwcnOBdee16CU3834XqJWh6Xm4EVpwxIB6TFdqeYammxgW2atduzNkCf9COX96k3pb43oYJNzfEs6h2mc0nNLX8WFocyRBs5vRN0y2Mkia3zPIhVn4FY8EIhCb1hU0UhmNpu0BDgCLlKJVMP4SZYgkn/obLtrZwN7H8qDf+nPihYQiDN238jcEgmLVVYktUFjOnw5YmWMkgW4nSF+o85FghA2uywiZZf07rtipCulMYJSldoX0Kvg2OBZaTrdU8DsmTz2Hoj7eRA+9ViCM0KdVqUki0aGofo06U/3zytYpHqURZ3V3F7zBWjSyWtV0xgaNLbBqhSWT72wbAAc1bo78ociTa+iYbgZ99QxGwDJP9AcezNMULZASYLjf/t+C8o5WUrgxWrSZeIgDQED+0zIh5MRNkU7NPYNUkZFM6gduYX3KBQ6fdc4AUEii4+VH5TRKKrvqLL2UexnHPRbV1c0MyE45zdE9byyYlm4rvypIWMVow/MqMwewP/28AQOqX/4X8PC4iWQCgJtgqjauCG39le0BdcRXpCoSVpl1sJtjrIOCVRWiz1jj5MSV3aJx3d3PDDMszcZ9dJzaN0CTNeoP0yZfIyNR5xuCwNOLCGMz5U9HX5UdJ1zVCnkQtFkpRiOciAOS7ASBbj1zG7xa0YWrySnFQnuDQaRcd6/pUHm6yoBQDxutTZ91znGe4+REYwqWV82DEfIlkASKRjZeeHpa6nR+Whx5zanblR+U7hrjXojYVJ6/i6vaD4SllIrw559WXcxgCqjJe/KSapUltXOUYKNa6y/Pd500YBmvuqQg4ToQ1yy2MStczo5QB0GBpUrHUWUmycqctTV0kbKMNi2vmDk6gERMmnFMYgzlHEJoKY7DYlibOR7vi5YvjMvWTYZFZiS80mZfeJF3vKGgO5ADW4Hp+HJTKJgTwBQ6+kDXM+y6JFp3mZ/SQ0MRIynA4CRyGIRUdJc24vUCtVBU8CAlQTNtWcGVxyow4+WF51ItXFdwtjMu2c7R5HgeVGJY446pcq85KyxjCGL8J1QIL+PsD71lRSpQ/L7nKonz3AdK+1vK53r4ReV1+RDlLrxtsOqGpnQCy0vDCNQO6+TFPu6TUPRmFNXcqumBYflTejxHcac6dVj74U5QmAIA9ERtx8p7QRLSQuYVROS6cquDMoFR/Q+IIoCpxbY3tyNdzhazcMCxOhe9MP93kHzxDoeQAE3+RNOyl9tcxhIF6dg217ADfygl4JQLa1GIK+sz53mJabkS2H8JMMEMK6C69lYKgf3CxpbhJclCyNHXA7UqBqhCH94EmPACex4L5rCjhJM536OTHlAVGvhLVm7WaNpfQVJxgDSjH3QY0bLKUDLrCmBcv0z7Dw/GtNsQPRFhpeTaPasXUkq89xlgIU3m46TJ5MXXzo/Lkbcap8E5hXC6kxMOF3YIUBs25X5KfoZKpBvACuwEZx6ByfzKGEXxHZKwU73oFqJY8lssjSBunlx1QWnwNo22GXN0KQ//e/N9DWQgxTLYF1mFYi9yV4+W7zrohNC3wi5BywxqA+DFJgBz7OEpnXaihxJmNwVyehmHTj9qKFJoKPKW3ue04LIV2si1NaOr1Wk2bSmhyCuPSrEd0PdUDUGnBnBytLdgwIhac+kJF+xD9zdG6RF+sG/EtTXFN7m5+lLyYqmg2bmFcxrkQ24h0ScYCcd5LIa4NUIj1iKh7tRIV1xnV9N1NAutfxO/kZodklhA5Ro5TFdwLvlapCt5GqXJzQxBmkrV51jerGBl0Oa7rlr5ehMX4dcM9Z9hLrCKkwkwqrYF1xTeGpagQL1DZX8MNwuHITpH/zfiWmtB7+p4ClQy6orqVzX9ulAAYq3ZXF9hUQpOv/YUtZCuXTTc/Sg5ABXgxMMFiFSU0BddRA8xjCj1F/3nxFkKnME4XmhTcYE7RcyMS46YAaWnkLqSyHg/dAibbdDZAVkUA8r/9XoIs/HnlCThC06oFP0QwdYsTMOxFpZo+TmEcZtj3ZJjSdcL43vzMqVhCEzcTNZEhx+C4+REpvPrv5Ama7NgXDo0nI7Bc6+MsK1/QLjfsCbtqSifgK+cz7EDqoA9BTBElUcgXcBjrtVcVPJRkVnoKFCxGcd6dug8ox1x1ic0lNHkbLXUhCzY/4mQV2QE54SjxAb5WGSFYsOODiu0Fw0gSmVjBfEE/CmNkN4PD/J3l/flBqEr+fxWrTofNx05xG7+NLzQRDxLuBhzhzynSXR5OcQLmpTdI1iP/d7EuvUHui49bnJB9CnmOUxjnudq8zKlYVoogBIFuOXOKEzQhwUrJo1paXasYQ8mBI8g4xQnpOuUeZ2KYUuBStNQDdUFG3U3lrfmEEAuq8r0SJ0KJaql4EIgyTLR/Js1qJjJ9EIkMOdGo22wqockp+EHOzRMizDnicOMS/JgCitbmFUGMEoZEuiTToqmWpnT8D8qJ6ZMHPAFl8RwMRmVmVoFLlZiRwjhbg4xaXFqhspBN3fcELr3987T7KwhNwe/FKLmwtO893n/x3IdURKpIvtYpTkpBiIBb3CYroBOsR/5vSb13c5+2wXCWQy3RbnGCvfnGLnBZ3AazNsc6sFYKCbT57ha3tfytOAducxFmAgBd2QU8CyKEWjCzgkW6+dnx4kJZykSepnxzn6HqYqwbAxSEJqIhAYYBp7gN1qWT7Gd0g00lNNXNyyEDukJjdEtyQbVm6QuqU9pGm3Bmwsvmi/rYZRAvzdIkvKDf8VhxBi7DtRZGIHBSrC2JrLRuMTYukSpIEzJzITUXL5ADtGWbSfK19b6VUBu5DrPv+CK5jT12A5aufD/pWt81yUHFMnfpjn+L8w++yI656gSBa5Vg0fAtcRTrUf1aBVfOKoGr+XeSyQqnyckKgB9EHMM1pCAE1tcLgmUuRKCIq2S1I3CZMTbJugVRZVwnlITo4NkqLrPG53OK4yaz3vFdvGf5v48RZiXNq8UmBb+7Slvf41CLdu2pKCTdYlMJTbCSnnmZ9oGJdBluqsSarG5pO8zZ11vcbPXHKd1FBKtUYZzlPoifvUEP4g6/hxfU59KKxDnFSVizPM3BLTCzIQt8DdAp8a06MAxMv/dvUb3sbn5bAtxsO6AhQYAzrmYCItPHfhaH6thNqG4/FHmdU5psa9VphGM9kqb+rNICXI+ra93WLUzAEA4vm604Kb9p1eKKRb6i5xYnPMtcdKC1zEA+tUoQVMm0ImNYbJdZEIqhIIA6hQmp7IVYycMEjeDZhZhZkKbFutwtjMNguGP9NgBgLLV2AbqlSVnWhJGVB0i3noChJjAmcwAAu39/5KVOcVJbmroFV0J1SnS3ACA1PTnhoq0ZToipe3UftrMECqcwobRY1NuPw6zOwqjOKd+DG3jslLbDZE4Cx48pYfaJ5dJTsDR1HAXLT7BI9lhBuJn7/gozxx6LvM5lWIRY1iPD8KwnajFNQLhwRnY3NLYpTcrkE0WlxylNtu1Tyzb+vCD+toZbW5WSH8edRcEpbmOugb6lScFNVNxGcO21mYNBgcsYLr7sAGrDB0nXssuJoP7thq2f/rrHEb5lg5Q8skrx+734wecw/Z6vRV7nFLfJo2AUg+07yaYTmtq5rlrJ6i53spa2w4CgpfCWtsvFOsJ875S2Sw2b+IHII1rOxtZWuUJMI1wXklua9NwvPFcG55iKukmaHrTqqliauoCbzLOuF+lyh3rSHQK3E2Eu1q1H1BgoNVO/tEQXQ+d6lCWqZZvSdq+NmntIpCsQiRzbMg1Ans0YeW1jMk1DZlsMdxYFp0RTMAP8hJZYFsQYLroS33LeyIUHfoDp9z1FfNZ29v2jBOXgO2zlNYlAekbUBEansptk3XYZLvhus+mEpsB1Rdw4g4+fWMeF4xpwStthuHZ0raZgIaVNwkBTUtZWd8jnzbym1B4AkMgGRchIzyxOSg2WkXHmFHnWLLcwRj4UMmijcG5bN2AHgxsGlnfdiblbf68j/ek0HEtTPVCUtqA6hW3KcUTt4l+iLFEt+1LyNXz+ZgVAvntpkic0MRScVvFibqoYb60g4Ba3ee6i9tXjG3GY7vv6s3hZ1i2fXdquPoYAy5rslnewb+9635mwMm3/XUVxdso74r075RlaaOoeTnmHjI1o3JzbfJ9ucZusVk04gR7gSeiBcDLbfsHhLqROmXbfyPYxF0KntJNxrfeOl+iTzS0yNaxEhl/jyOjNKeBrwwYhaNJn9t3/DosHP9KpLnWUeuA/VXGgW4/8BAHUePEbgOcmCBkDkSrCzfSxNhC3MC6PgYlhpXCYgcxubggi0XrzXH3v1UqhU9rRcaEpcBdx3PHFCdpvv0IhjlOGIrhHeaf8/hSt/axnlRSEpvwIZo/8ES7d+cet/z03DGGllaxlTmmH/D6IRaFV8D0AnbZwqtCbO0YMnPJOAIA1cyL4WyAztTAmBZOVuljnR+X5TwRfcF24aT+x68IVVWjaKa9veEcOItMnN6iY2oJbpgs1gWbDyVSs7OR2KfhtNjq9bJ7uFE5xknxcBCcGpq618jeIKCuNU97Jm4dmQlrDY8w9tzjJK8hqmPSNN5WHm640W5rK26WCplBVnQrZatfQB6e8U34DpOzFBs05kYGTG2atRStxSttlEkAX5qeKew4Alve9J7wOnW+xVPgOnfIO7907F+cmMypTPbn+bQmhyaeV6yYIrGxcgNstDqYls7oIC7BbGIMwE62z7Rr7lemDm8xHXhfcNz8itYQY2p9T2gFr9oRye/8eQPSBrIDUjgUMZvwYX8NyFEzZIpFjt+k0S1f8CwBAdfyfrXNPugcnvswpbZdnchHqFcVRMpzyrsh7c+8r3WtxLE0KxU8ZysTKd3JKnvWekSXIpR6YTN3EDTiVXTDcqpKLzinvQmLmVXY7H5eoEK8FqkIT5b4qFk83pqeDhGHCKY7Hssh2ik0nNLmFcVnzg7iQuW1dY639elLLJUwWMwG3QDAhG4YMGqdOQMNUWqwbcco7YMZ2z/muSsKHbaXh5kdguPQKviJd4vfJ2xxYhwN734BBPLOwG9jDB3HuY2/AGbpqvbvSNaIElKZrK7sBANZ09MbnVHZ51/5izftUd9PQsxad0mQ891wsCyyhDlZld9Pvulbu/Ha4hTFPETxBblMXhvnCj1PZSfp2QtsTQy/WhGS2I7d1VS1NaxETS3lOeZfS2HaaTSc0wUxITa6lpWk1IlWEmx1kLahOeQd5cstroz8udtmBmEKTW9rhZfap+6X9xdQkLhxKliNmSYBgIWUsZvM3/ivZtgcPvd1K+IIQ51rKoioyfTL2SGGT9AWu0H8v75TZtIy565a2y6BnhRgr2ac9/Dae8EepRu5UdsGa+2XgKvVjF6mWcB7eqmxack2b4qzDnjCsaEE0F8+xlKtG1sLav944xe2y9MwS71zGbr27U9mNxPQvOuoWVmHzCU3wtD/GRLIre5CY/jn9/n2XwVyaop1SXdpB2sBlsOWJiKy/lf7815TPhHLKO7zMvhjHsXgCCqXCKwA4fSqL/U7m9XIhpbgMfap73o1zH3sDIjvAepZmbWEJTeUd0t1LVHZUtdYooT3U9ddmobc9oUdVi1ZxQXNclHWBVF7rFCcgDDOWktYWL5PMqeyCNUMXmtz8CEQioygM0y2VLTFMT9HdwEKTqgXWi5Hr9Ls7ld0w7MXOFlZVYNMKTVIAkQtXVHKn07cH1hRDaPIXPYKg5ZR3SgErIjvP6dsj4waiAt/8Baa8A4a9pHxo7FqY3EWmn/dMJQ15JwC6EKRizdL0Bjbn+7DSMk2dKjRVdiu552Al29+3rbUjxL3vKQ+JqVf4/QEAK81u4m+QlMrSqwQKKyWD1zslNAXP3SPXI6r1O0aYQiAwxHD/cDwOcQnKu3APKG53z77LAADW1M/4bb0s9U5S32d7y0W3aYUmszYPY8WRDCLsHJ7KHphLF8llB+xg0SMITSEf5spS/XbfXnnPi7SFNG4GXX1hpAuLqzAMLO1/Ly4d+iztmd5vwSGwZlGFuxSvKKSmd+Ba+lbG3rS/dpc8MFrBJdYuXV9k+uRRTIzN1ynvklYyhc3Kx2ZY5QDmIbHl1RYIqmKZfvmryrGSdmW3rOXWhTAF3+UYK66pske2Z5w9qMrMr3wFS5e/j30SQzuc0nYIM4nEtIrQRI8/VKW+RykoOx1kkwpNckCpLjdf86MOjluclOmQhI/N7t8n+3Lxp60v8C1HvnBF7bMvhTMsZI24uRG4qRJZSAvj0jv+dyxd/euka20V95ynEcbJdNFsEJjHxziVXQz3HD0GKqxtS1e4IbO4WPMwkZFxTYpzF2h0ZRLjPRj1yESqACc33DTn7L790jIWISCUnvlt9P/Ht5Of1Yj/TglWfOkuzzrFFFySWTiF8Vgbst2/D4az3J0Muv59uHTHv2WfW9cWMyF/P4Xv0PH2tU7iFkY992sMxb4DbE6hyTsQ0AoTVFZe70u0VM3PtMgLpVucgEhkIxGD9QoAABsYSURBVO8tsv1wM/2wiCZ7tzAON1lA4uLLpOtXYRhw+vfCuvgTtfYKuArnvFW33Ybqttswd/MnyW0u/Po/4sKH/zP7WZqNhV3ZTToaBIiXQRdYdUKCqJ3+/eS1Jrhn32VIxLA0+UoTxz0/884vYebdf0a7/wpLgtO/lxY+ALAyZJueUeEpr4AnuLhVYEpFGKYL3S3be94B6prdi0gLIr//dv/eDvRmBYYJp6zoVu8gm1JocgtjcFNFJC7QBIK6mZITDL6HJgEbprdARi+qdt9e+kJqGHD697EX66bn9e9Tj6tQQUVLSmYxc89X4AxeSW7iFieCo2k0G4ug9hfBjcaJkbMreyBgIKGgJCzvu1f+R7m10G/374e1cJbs3gekZdma/rl6IodvgWVsKNU970Z11xHa/VdsipEW8zUgKLrLWYc9Bdk492P28+z+fVJgUB0D7zeyYlrr1xO7b6+01DFjpbphaQKkwkIJg+kmm1JokgLFflhUK4yZkL7xi3TNz65cRv7YnL7LSPeW171CTrG0+/cpbQL15+2DuXgBBuNQXI2mk1w6/HnYld1B7ax22ANX0G+czMKp7ELiAt8yW939Tpz/l8chJlsXGq0LFPS5SE78COvTzncAAJZ8gW6Naf5tjYbYTJrQpLSmeOs253e0+/ZCwIBx9kfsxzkDl8OszSvXzBKpIpzCGEkh7lWcvj0whMOOCxOpYmc6tILq7nfCHri8K8+isjmFJkjtL3HhJ4CgHeFqD1yBxAW6tuIM7Jcfm2cZapeV4vTtk3VPqu1dCU7fZbLKMXHBcfr3S6Fn4XzIFe2Fr25ojyupjbzF+y9e/Ipma1CbuBlTH3wOSEZXaRe5IbiM4HFn4HJYjDne9KxseKaoM+CFAxAt20Bj4ofa3HPzozj3sTdQm7xdqX0UzoqNSqTLcPKj5BhIFeEUAOzBK2Cd/zHd+pPMykwuFUuTJxiq9hXw1vYNbGkKhOEu7gEclvfdi9l3/7v17kYTm1doGtgPc3ka5sJZ2vWDV8K6dJJ0LIO8XlZqTpxv1HBaCwJ2YMZt/2H6EjVVeLO9xbq9ZhYunDjEfq0l07/yFUy9928jU7k1mkgMg2VtsgeugDXz2ppXfnfzY15SBcPS5G/Y53+4pn1ZK1pp95xwAGWhaeBKmLW5Ntaf1Yqg079f0T23X7psFQVpeY+95Li6XsTu3wdhWCv2MWJbT/BXdW9uVDat0FQPBq8vZO3sLq2FoDb3L++CSGSQOP9S5LX1e7dfIO2hq+V1516k9cGzFKkGcweLfYxFg00qD3vkuu49T7OpsQe8WDfCwm0PXCGrd6+1kmAYcAb2s+ahSBVgl3cicS56/VgPRLq86m+27zoj1FFSXZNsL3YxcaH9OtzoP7AHLgcu/gJgFLQFIA8nLm2Ptf45/b3lOmKTyMLpu4y0j63EV1g6eSZhL7JphabAanP+RyRPkB9oTJa4TUu69AiaolvcBjddjhSGRKYPTmGCvJC6uRG4mX51bdUwYA9dTRbSNJpewx6UCzclJiNYEzqQ/GD3X44Ex60EwB68umctTa2whw/IFHuC0KkqiATWn/OMUIn+y2EIRykb0R68AlYM91xt+IBy215B7gH873Du9s9g8coPoLr9cAd61btsWqFJZAekAHL2OOl6Nzcsz6BjmCntwaukkBUVuG0YsIcOkIQTe+jqcKl/5XMMA/bwASTP/ldij1s8b/iAXKDWsNKsRtMt6m6u6HnrlrZDJKJjpVSwhw/ArM6yAmrtoathzb5ODgnoNm52UP6HV/nZHpICAmUdS5z/EeDU+A9N5ryAffo6XPMt9ArroHTZvgooumz9sgMbGXvwalgLZ2AunmO1E9l+zL39Dzp2oHCvsmmFJgCwRw4iefYHtIsNwxOC6BK3PXgVzOUZecp51LVDV0s/f4RwYg9dg8T0L9ocJNlsNqsNH5San+Kkt4cOwnCrsbLwNJr1wo8XrE7eFn2xYaI2fE1H+lEbPggASJwhrjfghwR0m9k7/0/Uhg4E1cSdyi64yQKSEYqoXZGZgaQ1pYXCaQ9exXJbuuWdEJkKEtS1vvFZwwdhCBfJczTlehVWEm4LV+ZGwh7yvkMFa9NWZFMLTbXha2HNvo5MjXaKsz10DRIXXyafcxYseueiNRx76AAMtxa5kNhDclGnLqT+pFeNjfA3ERUtTaNZd6w0zj/4IuZv+V3S5baXvWnYa3tultO/T8Y4MuZRff3ozbim2sTbMP2r/wnwj5ExTM+VEyJgeAKQH7NIFyCbFUF75DpYl96AMU9L4oFhQIy/Bckz/DWs5vf19D+x2/r4FriNGhDtf4dmdXade7Ix2NRCk+1pf4OXaAJIbewGGK5N1ljsoashrDSSp74XfW+icFLzhSbiguO/o6qLzi3tkPFWZ3Vck2ZjIjJ9gJkgXetvklxXRCRmAvYQz1Uu8sNwituQOB29fvQK9tCBSNebU5qUsZZn1QSR2uj1AIDkGfrvIsbfIoPPW1rcw8MnRHYATmkHkop9BQDbW9u7dXjvWiPSZfZZhluZTS40HYCAgSFPaBIRdYv8GkJJ6iJmpWAPH0Ty9HciL3VLO+BmB5A89V3vL637IvLDchKfeoHUBTc/IuunKJimAXhxUQeRPPN9tfYazQaik5mbteGDMt6HEctTG70ByVPR60evYI9cK11vbcMYDNRGrkWS4apsesbQ1RBmir4OAxDj18tg8DCrXZtzDWsj1yJxRl1oWt4hC426uWHle6w3tbG3yv/YoNaybrKphSaRKsLp34ekQ4v3Edl+2JU9SJ7+bvTFHrWxG+REjXLpGQZqYze2EIZWT+ba+I1yIaVWBh+9nnX9queN3Qjrwsswl2huTI1mo+LH53QCe+QtnkBBd7fVxm+EtXCmK4e+rgW18RsBIFKps4evhXXxpzCWFVw+Vhr20NVInqYrcmKcqfA2YI+8BdbcKZhzp9htAcAevxEXP/Aslq78gFL7XqA2JsdVH4wezaYWmgCgNtH66IPQ60dvQJKhddRG3wrDrZHM8rXxm2DNvg5z7s32143dCHPpIvkMpurE22DNvQlTceGtTfwzGBBk65ZGs5ERZmcKq1a9tSb5y/9CblMbvUG2OUVX1NYTNz8Ku7wTyV9+u+11tfGbYq0ptdHrZSgDNas3PwS77zIkf8k/qDtwB8ZY/5y+PWpna/YItm9p0kSy6YWm6sTbgv+mGGLssRtY96951xt29AGjtfGbAADJN9tPTl/qp07i2sTNAICUwoIByIB5YaWRfLP9QqjRbAYuPPB9XPjQt9b8viI3BLtvH2seOv374aZKsC6pnX+2HtTGb0Ly1D+2cOXUF9ja6PVyTXlD7Xeujd8Ew1lmhQ3UJm5B6s1/ZJc6sIeugZsqIfnG89xubhqc8q717sKGYdMLTbXxm1nXV7cRUpcbEJk+1LzsgyjsgStlym6EcOJUdsPNDiH5Bm3xdfr2ws0OsjTcJhIZ1EauUzJtazQbDZHpg1ve0ZF717bdLJUiQtVsAIBpBcpUu/Mre4na+M0wl2fCi0IahlxTRm9ASlVomngbhGEhefKb5DbVbW+DYS/wM4FNC7WJm5E6uXWFJhgGnA0ck9VNNr3Q1O6gzVa4pW3suhu17YcAAEbUGUSmhdq2W5B67R/Q9lAXw0B1+yGkTj4LuE50BwwD1Ymbkfzlt9TjmhoschqNRo3qhNy4LcbREtXt/7xzHeoAgWU7QqCpbbsFiQs/grF4kf0MkS7BHrkOqdefZfRLrmGpX/IFterkbbAunYQ58xq77Wbh4oe+hfMf6c2aYb3EpheaAGA2MwEAEMTX9Rcxw6HVcvGvp5Txr+44DGvul5FHEVR3vB3m0hQ5K642eQjW/BlWRfOm5+28Q6mdRqOp41tIOGw0ocktTsAeuBypE19ve1112y0AgNTJ55SeU528DYlzx2EsTTX8tU35gEwfaoNXI/X6N9jPqm27FUC0ILipSWYh0qX17kXPsyWEpqdu+HN8pPqvIPwibRFUJ6XliGrm9YM5SffeIc/pSZ14JqIPt0MYJlKv/X/N/xCSOru88x0QMJB+9e/IfWnEL6qp0WjUEZm+evo2kU65CjvJ8s4jSJ76Dow2Gbf2yHVws0NIKa5J1clDMITbWugKWQeru44gceq7MBbOs57lVPbAKW1X7qtm67AlhKZqsoyn3evJ1y9fdjeq2w9h8drfoDWwUqiN3oCq56Zrh1sYQ23wKiQj0pJFpg/26PVInXia1AWRG5TXv/r3pOtXYZiw+/fL/1Q5M0qj0QAAqrvfCQAwZ+muHv/E+I1Cdec7YAhHhhCEYZhY3nVUKn7EUxYasUeug5MbRvrnT5HbLO96JwwIpCOsYKswDCzvfhdSbzzf5ggrjWaLCE0+5HCfZA4zxx4Lqm1TmL7/Ccwce4x0bXXXnaTrlne/G8nzP4Q1RSs9sLzrKJLnX4I5+wbp+pUsXflr8j9cLTRpNKos7zoKADKTi8jU/X+Di+9vb33uJezha+FmB5sFmhYLbHX3nTBr82oB4aaF6p53Sat8dZ7UxBm8Ek5xUslitLz7XTDcGllR1WxNtpTQ1Css77uXdt3eY9Ll9soTtOv33AUAyPz0caV+LR54ENPHHiMLdRqNZjVuaTuq227FpUOfozdKZuEM7O9cp9Ya08LS3l9B6tWnW7jo6q6z6rZb4KZKSL/y1yE3aq/JLu+5G4azjPRrRIHStxi9/iw7AN0efQuc3AisuegD2DVbFy00rQMO8ZwfNz+K2sTbkP7p4yQzmVveger4TUj/5KtqWXSGITMBN3CRNo2mF5i55y+wdPWH1rsbHWV5//0w3CrSP38y/CIrjeW99yD9i/+kVB28NnYjnPwI0j/5GrnN0hXvg+HW+MqjYQYKraGt7ZoQtpTQ1EtVUBY9V5iw2lcnXtp/PxIzJ5B8k1aDaenyX0Vi+hcb6hBQjUaz8bCHDsDuuwyZl7/a9rqlK/4FDHsJ6Vf+hv8Q08LSFe9H6rV/gDlLKwDqDFyB2vBBZH78F2zlcSMfhaLpDltCaGpzVuO6MXfoc5i6/2/glne2vW557zG4mT5kj/970n2re+6Cmywg++KfrUEvNRqNJgTDwNJVH0Ly9PfaHnhrDx+E3b8f2Zf+g5IFfOnKDwCGgcyPvkJvc8X7kbjwYySYB5E7fXu43dNsMbaE0NSTmBbs0bdEX5fIYunKDyL16t+T6kaJVAFLV30Q6Z89uaGOZtBoNBuPpSveDzdVRPYHf4pQW75hYPHgf4vEhR8h2S7bLgS3OIHqjncg+8M/h1GjBYQv7b8fbrqC3Pf+mP28qff+LeZv+h/Z7TRbgy0lNAnFatnrzeI1/w1gJOjXH3wQMAxWNV2NRqPhIlIFLF35AaR//lQ9y7eFaX9p/31w8qPIfe//UHrOwvX/HcylKWRf/DKtQTKHxQP/EukTfw9rnl6dHZClDhZu+O/5ndRsCbaE0GSgB/1zDNzCGBav/jAAwJr+BeH6cSxd/qsAAHPxXEf7ptFotjYL1/0mRCKD/Ld/P/wiK4XF6z6K1JvfXl2wl4A9+hZUt/9zpBiH6i4eeABussB+lkbTji0hNG0GFq7/LQCAU7mMdP38jf9DJ7uj0Wg0AACRG8LidR9F+kT7wrqLV38YdnkX8s//a0ChgO78Tf8Tr1+ZPizc8BAAIEU8/FyjiWJLCU0b0zknEblBnH/wRczfRBOGRH4Ylw79Gyxe9eEO90yj0Wx1Fq79DTiFMQCAUVtsfZGVwvytv4fE9M+R++4X2c+whw+g5hUcNoRLarN48EGIRBaLV+msOM3aQA+U0aw7ItPHun7pai0waTSaLpDM4dLh/w2Vv/kAIJzQy6o778DS/vci970/RHXydvZjZu75C2R+9BXURq6jNbDSOP8bP+3NFGrNhmRLCE16umg0Gk1nqU3ejqn7noDTv7ftdXO3/69InvoOyv/vb8BhHlYsUkX6maA+WmDSrCFbyj23of1zGo1G0+PYYzdApMttrxGpImbu/g+AayN5+nsw597sUu80mvhsLaFJo9FoNOuO07cHM3d75QNce307o9Ew2BruOW2d1Wg0mp7CHr0eFz/4HIylqfXuikZDZksITT7aO6fRaDS9A/Xwco2mVyC555577jnceeedOHLkCL70pS91uk8ajUaj0Wg0PUek0OQ4Dj796U/j0UcfxVNPPYUnn3wSP/vZz7rRN41Go9FoNJqeIdI9d/z4cezYsQOTk5MAgLvuugvPPPMMLruMVpm6l/jCN36OQmpLeSRXkUpZqFbD66ho1g89Nr2NHp/eRY9N7xJ3bK4aK+JDN2xbwx7FI1KCOHPmDEZHR4P/PzIyguPHj7dtYxhApZKL37s14vo9g7hyrISzc1WcRXW9u7OuGAawQc8t3vToselt9Pj0Lnpsepe4YzNQSveUPBEpNIkWb2tEpKMJAUxPL6j3ao0ZSJr48geuXe9u9ASVSq6nxkZTR49Nb6PHp3fRY9O7rMXYdGNsh4aKpOsiY5pGR0dx+vTp4P+fOXMGw8PD6j3TaDQajUaj2YBECk3XXHMNTpw4gZMnT6JareKpp57C4cOHu9E3jUaj0Wg0mp4h0j2XSCTwu7/7u/jIRz4Cx3Fw//33Y+/e9mcLaTQajUaj0Ww2SKlkhw4dwqFDhzrdF41Go9FoNJqeRZ89p9FoNBqNRkNAC00ajUaj0Wg0BLTQpNFoNBqNRkNAC00ajUaj0Wg0BLTQpNFoNBqNRkNAC00ajUaj0Wg0BLTQpNFoNBqNRkNAC00ajUaj0Wg0BLTQpNFoNBqNRkNAC00ajUaj0Wg0BLTQpNFoNBqNRkNAC00ajUaj0Wg0BLTQpNFoNBqNRkNAC00ajUaj0Wg0BLTQpNFoNBqNRkNAC00ajUaj0Wg0BLTQpNFoNBqNRkNAC00ajUaj0Wg0BLTQpNFoNBqNRkPAEEKI9e6ERqPRaDQaTa+jLU0ajUaj0Wg0BLTQpNFoNBqNRkNAC00ajUaj0Wg0BLTQpNFoNBqNRkNAC00ajUaj0Wg0BLTQpNFoNBqNRkNAC00ajUaj0Wg0BNZcaHruuedw55134siRI/jSl7601rfXhHD48GEcO3YM99xzD+677z4AwPT0NB544AEcPXoUDzzwAGZmZgAAQgh85jOfwZEjR3Ds2DH88Ic/DO7z+OOP4+jRozh69Cgef/zxdXmXjc7DDz+Mm2++GXfffXfwt7Uci5deegnHjh3DkSNH8JnPfAa61BqdVmPzR3/0R7jttttwzz334J577sGzzz4b/NsjjzyCI0eO4M4778Q3v/nN4O9h69zJkyfxvve9D0ePHsXHP/5xVKvV7rzYJuDUqVP48Ic/jHe9612466678OUvfxmAnju9QNjYbMm5I9YQ27bFHXfcIV5//XWxvLwsjh07Jl555ZW1fIQmhLe//e3iwoULTX/7/d//ffHII48IIYR45JFHxB/8wR8IIYT4xje+IR588EHhuq74p3/6J/He975XCCHE1NSUOHz4sJiamhLT09Pi8OHDYnp6ursvsgl44YUXxEsvvSTuuuuu4G9rORb333+/+P73vy9c1xUPPvig+MY3vtHlN9y4tBqbP/zDPxSPPvroqmtfeeUVcezYMbG8vCxef/11cccddwjbttuucw899JB48sknhRBCfOpTnxKPPfZYd15sE3DmzBnx0ksvCSGEuHTpkjh69Kh45ZVX9NzpAcLGZivOnTW1NB0/fhw7duzA5OQkUqkU7rrrLjzzzDNr+QgNg2eeeQb33nsvAODee+/F008/3fR3wzBw7bXXYnZ2FmfPnsXzzz+PW265BZVKBeVyGbfcckuThqCh8da3vhXlcrnpb2s1FmfPnsXc3Byuu+46GIaBe++9V88xBq3GJoxnnnkGd911F1KpFCYnJ7Fjxw4cP348dJ0TQuDb3/427rzzTgDAe97zHj02DIaHh3HVVVcBAAqFAnbv3o0zZ87oudMDhI1NGJt57qyp0HTmzBmMjo4G/39kZKTtD6tZWx588EHcd999+Mu//EsAwIULFzA8PAxAfvQXL14EsHqcRkdHcebMGT1+HWStxiLsek08HnvsMRw7dgwPP/xw4P6hjoH/96mpKZRKJSQSCQB6bOLwxhtv4Mc//jEOHjyo506P0Tg2wNabO2sqNIkW/mHDMNbyEZoQvvKVr+Dxxx/Hn/7pn+Kxxx7Dd77zndBrw8ZJj1/34Y6FHqO159d+7dfw9a9/HX/913+N4eFhfO5znwOwNvNEjw2f+fl5PPTQQ/jkJz+JQqEQep2eO91n5dhsxbmzpkLT6OgoTp8+Hfz/M2fOBBqCprOMjIwAAAYGBnDkyBEcP34cAwMDOHv2LADg7Nmz6O/vB7B6nE6fPo3h4WE9fh1krcYi7HqNOoODg7AsC6Zp4n3vex9efPFFAOHrWdjf+/r6MDs7C9u2AeixUaFWq+Ghhx7CsWPHcPToUQB67vQKrcZmK86dNRWarrnmGpw4cQInT55EtVrFU089hcOHD6/lIzQtWFhYwNzcXPDf3/rWt7B3714cPnwYTzzxBADgiSeewB133AEAwd+FEPjBD36AYrGI4eFh3HrrrXj++ecxMzODmZkZPP/887j11lvX7b02E2s1FsPDw8jn8/jBD34AIUTTvTRq+BsyADz99NPYu3cvADk2Tz31FKrVKk6ePIkTJ07gwIEDoeucYRi46aab8Hd/93cAZAaXXv/oCCHwO7/zO9i9ezceeOCB4O967qw/YWOzFeeOIVrZy2Lw7LPP4rOf/Swcx8H999+Pj370o2t5e00LTp48iY997GMAAMdxcPfdd+OjH/0opqam8PGPfxynTp3C2NgYvvjFL6JSqUAIgU9/+tP45je/iWw2i89+9rO45pprAABf+9rX8MgjjwAAfvM3fxP333//ur3XRuUTn/gEXnjhBUxNTWFgYAC/9Vu/hXe84x1rNhYvvvgiHn74YSwtLeH222/Hpz71qZ41ZfcarcbmhRdewMsvvwwAmJiYwKc//elAy/2TP/kT/NVf/RUsy8InP/lJHDp0CED4Onfy5En89m//NmZmZnDFFVfg85//PFKp1Pq87Abju9/9Lj74wQ9i3759ME2pz3/iE5/AgQMH9NxZZ8LG5sknn9xyc2fNhSaNRqPRaDSazYiuCK7RaDQajUZDQAtNGo1Go9FoNAS00KTRaDQajUZDQAtNGo1Go9FoNAS00KTRaDQajUZDQAtNGo1Go9FoNAS00KTRaDQajUZD4P8HadI3OO/NK94AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(temp_df[\"EVHTR\"], label = \"EVHTR\")\n",
    "plt.plot(temp_df[\"EVSMA_EWMA\"]*1000, label = \"EWMA\")\n",
    "plt.legend()\n",
    "plt.title(\"HTR vs EVSMA\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5605457465519547\n"
     ]
    }
   ],
   "source": [
    "a = temp_df[temp_df[\"EVHTR\"] == 1][\"EVSMA_EWMA\"].values\n",
    "b = temp_df[temp_df[\"EVHTR\"] == 0][\"EVSMA_EWMA\"].values\n",
    "effect_size = cohen_effect_size(b, a)\n",
    "print(effect_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WM5/cSnR+Zs+eh8HBYxgZGRKrQSmVV02f4wQxe/aFjZXhGYCIsqauzsHIiEJ1dXZOxHPmAL//vYO//usEFi06uXB2Yz2chr3w5s9sCOr5shUV0L1HEHjlJSQ+NPlatUTpZIyDuXMvFq2BT6aejbcXiSgrIhHg0Uezc5XrFGMApVJXuwAA1qL4wR/BBoKZv8r1FrasHCU/vh84OaCRiAoTmy4iyopnnnEwOpr+ERFTmTMHeOklB93dCs6eXXCaG9M+ImIqtrwc+mg/AifXfyOiwsSmi4gybmQE+OlPA6iqyn6+Q+vURa2Htzgo+fH9sEVFWb3KdYotL0fJgz8CEjNYG5KI8gKbLiLKuKeechCLKRQVyex/zhyL8O92INnYBjurSqQGW1YOfeI4glufE9k/Eclj00VEGRWLpQaVzp4t9xST1sDNAz9G/1CxyFWuU2x5OYoffohT6okKFJsuIsqo1183iMWA4IWNt7kg1aPduDzRhMOR2UgK3t2zZWXQgwNwGvbKFUFEYth0EVFG/fKXgayH5890zdE6KAVYpXDihNyVLgCwCgg+87RoDUQkg00XEWVMT49Ca6tGZaVcDY4Xx3VH/wsjzmwYA/T2KkBwXqOdfRGCr7wENSw3tJKIZLDpIqKMee45B4CVjFHhHUOvo8iNIqGL4BggNqYwEhEsyBjA8xB46QW5GohIBJsuIsqIRCI1m6tK5mHBcR/qfwJJ9ceFrxWAo/3CtxhLy1D8X78A8miJFCKaGpsuIsqInTsNRkflxkQAwOyxPiwd3okR54+dXyAAHD+u4AoOh7dlZdBHjsDsa5Urgoiyjk0XEWXEU085cBzZKzl/euw3UNbCqj+e6pQCPAsMDgpe7Tp5vzX462fkaiCirGPTRURpd/Sowu7dBrNmydWgrYsP9T2BiHN2EUafDNQL8mbPRtHW51KLUhJRQWDTRURpt3WrAWChBc8wS0K7UJYcQtyUnPU9xwEiUYVoVKCwUwIBIJFE8NVXBIsgomxi00VEaeW6wFNPBVBRIVvHdX1Pwk5xijt2VPYUaIuLUfTLJ0RrIKLsYdNFRGlVX68xNASUnH2BKWsqEgNYMfgyQoGLJt0mEEjdBpVckcdWVMB0dkB3dcoVQURZw6aLiNKqrs6B1rJ5qauPb4WCB6vMpNtoBbgeMDQkHKi3FkXP/UauBiLKGjZdRJQ2sRjw6qsGVVWyTy2+79ivMKZLp9xOK+D4MeFA/axZCD7/HGd2ERUANl1ElDZ79xq4roLjyNUwK34MtdE2RM3UoTLHSV3p8twsFDaZ4mKoUAhmf5tgEUSUDWy6iChtXnjBiC75AwDvHHwVgMV0Cjl5dw/DIeGiXReBP7wsWwMRZRybLiJKi3gceOUVg1mzZG+T/dnxXyOuis/rNSdOSN9irETwd8/yFiNRnmPTRURp0dSkkUgoBAJTb5sp5YlBXBquR9SpnPZrAgFgYED2KUYUl0CfOA7d3SVYBBFlGpsuIkqLl14y4hdq3jH0Bixw2rI/U1EKsB4wEhZ+itHzEHjtVbkaiCjj2HQR0QVzXeCFFxzxpxavOf5rJNX5X2qzkL/FaMvLEfwdR0cQ5TM2XUR0wVpaNGIxIBiUq6E4OYIrhndMuNbiVAIB4PgJBSs5KLW0DObwYejDh+SKIKKMYtNFRBfslVfkby1eMbwdgD3nQNTJaAV4LjAyInyLERaBN16Tq4GIMopNFxFdEGuB5593UDn97HpG/OnxZ+Hh/BuuUyxSgXpJtqQUwd/+WrQGIsocNl1EdEH279cIhxWKz29KQ1oF3BiWD/0BI4Gqmb+HAxw/rlLdlxBbXg7T1QV19KhcEUSUMWy6iOiC/OEPBq4re2/x8tAOwHrw1MxH4WsNJJNAJCK/FmPgTd5iJMpHbLqIaMasBX73Owezzj+7nlZ/cvx3AC68WfLHLcZiBJ97VrQGIsoMNl1ENGPd3QrHj8veWnS8OK4afBEjzsxvLY6/lwMcE7/FWAGntRlqcECuCCLKCDZdRDRjb7xhYK0VXW/x0vBeGJuEqy98FL7RQCIOjI6mobCZ0qnTcmDnDsEiiCgT2HQR0Yy99JKD0lLZGpYNvQ5tk2l7PwsgJLwAtnUcBF55SbQGIko/Nl1ENCPhMNDRoVFWJlvHuwd+j6hJX6jMaB9Mp6+ohLNjeyrZT0R5g00XEc1IY6OBUvbU3TARs8f6cNFYL8Z0Sdre0zhAOKzguWl7y/MXCEAlEjD72wSLIKJ0Y9NFRDPy2msG6Xhi8EIsGd6VqiGNobJT7xSWXAAbADwPzu6dsjUQUVqx6SKi82Yt8OqrBpWVsvO5rhp8ES5mPptrMhbA0JDwLcbSUgRfekG0BiJKLzZdRHTeenoUwmGFoiK5GrSXxLLhNxBx0r/+kOP4YF5XWRlMVydUaFi0DiJKHzZdRHTe9u418DzZGmojrTBeekZFnMloYCwOjI2l/a2nT2tAAU5DvWARRJRObLqI6Lxt22ZQVCS/9I+2mU27h4VHRwAKzuuvCtdAROnCpouIzkssBjQ0GFRUyNbxnhPPI2YyN69CK+CE8C1Gr7ISwT+8nArREVHOY9NFROelpSV12jBGrobSxDAWRjswmsGmy3GA4SEFK3kbtagIKjICfaBbsAgiSpdpNV3btm3DmjVrsHr1ajzwwANnff/xxx/HjTfeiHXr1uGWW25Be3s7AODQoUO48sorsW7dOqxbtw4bNmxIb/VElHU7dhi4kjOsACwJ74GFAlTmfm9UKnWBKRLxweiIvbtlayCitJjyWWvXdXHXXXdhy5YtqK6uxs0334yVK1diyZIl49vceOONuOWWWwAAW7duxcaNG/Ef//EfAIBFixbh6aefzlD5RJRtL79sUFEhe7tr+eDLWdmPtcDQMFAueCvVFpcguO1FxNd9Qq4IIkqLKX9NrK+vx+LFi1FbW4tgMIi1a9di69atp21TXl4+/u+jo6NQkqvfElHGHD2q0NurUZK+AfDnz1pcObAtI6MizmQcYEB6SaDycjiNDcKrcBNROkx5pau/vx81NTXjX1dXV6O+/uxHmH/6059iy5YtSCQSeOSRR8b//NChQ/jYxz6G8vJyfPnLX8Z73/vec+7PGIWqqsyvoGuMzsp+KLt4XDPrtdeAQEAhEMh+HFQpwHEMaqKdKPVGMFRUnfF5+AEFjMYUAIVA+mewTo9jAEdjdk877DV/JlRE5vAzm794bM825WnETvDUzERXsm699VbceuuteOaZZ7B582bcc889mD9/Pl544QXMnj0bjY2N+PznP4+6urrTroydyXUthoai5/ljnL+qqtKs7Ieyi8c1s557LgjAQTKZ/XS54xgkky4uHXgTsB6sl6VbnBYYGPBw0UVyt1R1PImx37+E0ctXiNWQKfzM5q9CObbz5k0/fzDlr6s1NTXo6+sb/7q/vx/z58+fdPu1a9fi+eefBwAEg0HMnj0bALB8+XIsWrQIXV1d0y6OiPwjmUyF6KXzXFef2Iq4Ks7qPgcHhUdHVFQg8Ep2cmxElDlTNl0rVqxAd3c3enp6EI/HUVdXh5UrV562TXd39/i/v/jii1i8eDEAYGBgAO7Jx5x6enrQ3d2N2traNJZPRNnS3q6RSACB9A+An7aAG8Nl4XpEnewl2x0HGBxQqQUZpZSUQB/tg+7vm3pbIvKtKW8vOo6DDRs2YP369XBdFzfddBOWLl2KTZs2Yfny5Vi1ahUee+wxvPbaa3AcB5WVlbjnnnsAANu3b8f3v/99GGNgjMG3vvUtVFVVZfyHIqL0a2zU4qMiFkeaYaFgVfaGhGkNxOOpobDFUg8QKAUoBdPcBK+6ZurticiXlJ0otCUokXCZ6aIZ43HNnH/8xyLs26cxa5bM/h3HYFX3g/iLQ/djIHhxVvc9Fgcuu8zDvHlyp0t1/Bjiq9Zg9J++JlZDJvAzm78K5dimNdNFRJRIAE1NBud4BiYrlg++jJjO3BT6yWgFDAnnumx5BQI73uCSQEQ5jE0XEU2po0MDsKJL/wTcGBZFWjK69M9kjAMMDwvnuoqKoAcHoY/2CxZBRBeCTRcRTampSSOZlK2hNtIKq3RW81ynaAW4LhAby/qu/0gpQAGmpVmwCCK6EGy6iGhKr79uUJzdKQ1nuXR4N4wn1/lZACNh4VuMSsHZ/qZoDUQ0c2y6iOickkmgsdGgLPt39U6zfGAbYkZuurVSwOCQD3JdO99krosoR7HpIqJz6uxM5bkcqWVwkMpz1Y60YNTIJfkdAwwP+SDXNTAAdeyYYBFENFNsuojonBob/ZHnAiCS5zpFayDpi1yXgtPSJFgEEc0Umy4iOic/5LkuC++FscKTWU+SznUBgLNzh3QJRDQDbLqIaFLJJNDQ4IM81+AronmuU5QChoRzXV5FBQLbXxetgYhmhk0XEU2qs1PDWuE8lzeGxSNNGHWEJ7Milesa8sO8rhPHmesiykFsuohoUn6Yz3VJZB+gsrve4mS0BtykH3JdmrkuohzEpouIJuWLPFdoD4znjzzXKb7Ide3YLl0CEZ0nNl1ENKFUnkv7Y71FUyJbxFupk7cYBXkV5cx1EeUgNl1ENKGuLg3Pg2iey/HieNtIs+h8rjP5Y15XMXNdRDmITRcRTaipKdV0SfLDfK4zaZ26Cjjmh1xXK9dhJMolbLqIaEJvvGEQDMrWcGm4HsYKJ/knYAGER6RzXRbOTua6iHIJmy4iOksyCdTXy+e5Vgy+jDEfzOc6ky/mdZVXIPAmc11EuYRNFxGd5eBBBddVonku7SXxtpFGX+W5TnEMEBIO06O4GPrYMajBAdk6iGja2HQR0Vn27dNwhac0LBjtAAB4PspznaI1EE8AibhgEUoBRsO0tQkWQUTng00XEZ1l1y6DQEC2htqRFmifrLc4EQVgJCJ8tct14TTWy9ZARNPGpouITmMtsGePQVmZ5EwEYNnwG3Ah3PmdgwUQDsk2Xba0DAGG6YlyBpsuIjrN8eMKw8NK9slFa7E0tAtRH6y3OBljgKFh2RpsWRlMx37h+RVENF1suojoNO3tGlpbKMGLOFXxoyhLhpBUwjMrzsEYIBpVEF2hSGtAa5jODsEiiGi62HQR0Wnq6zVcV/a22eJICywURDu/KSikyotEhWtMJGH2tcjWQETTwqaLiE6zc6dBeblsnuvS0G4oKzwOfxqsB4yMCNdQVITAjjdliyCiaWHTRUTjRkeBAwc0SoTXl1429AZGfZznOkWfWodRkC0vg9NQn3oCgoh8jU0XEY3r6NBQykILnhmKkhHUxLoR0/6bRH8mxwDhsPDi14Eg1Ogo9JHDgkUQ0XSw6SKica2tGknhpQ4vibalBqIq/5+elAI8D4jF5Asx+zkklcjv/H9WI6Ks2bnToLhYtoa3hRthPP8tcn0u4kNSATh7dkmXQERTYNNFRABSV2waGzXKymTrWDb8Bsa0cKjsPEmvw+iVlSGwa4doDUQ0NTZdRAQAOHRIIR5Xosv/aOvi0nC9Lxe5noxxgGHpxa9LSqB7e6HCIdk6iOic2HQREQCgrU2LPwA3f/QAtHXhaUe2kPNgNDAWB5IJwSKUAoxhrovI59h0EREAYPduI/rUIgAsGmmBhn8XuZ6MAhCRznUlkzDNTbI1ENE5sekiIgCpRa6lh6JeEdoOD0a0hpmwAMJh4RpKSzkklcjn2HQREYaGgKNHFYqKZOu4PLQTUVMhW8QMpBa/Fh6SWlYGZ18LxGd+ENGk2HQREdraNIyRXeqwIn4Cs+LHkNDCnd8MGANERhQ8yZWLjAEsYLq7BIsgonNh00VEaG7WcIWjVIsiLalbiz5e5HoypyoelV782k3C7GuVrYGIJsWmi4iwY4dBaalsnuvt4QZo5O6tMQtgJCJcQyAAZ+d22SKIaFJsuogKXCIBtLcblAovdfiO4TcQ07kzn+tMWsnP67Jl5QjU7xWtgYgmx6aLqMAdPJhqFIzgQ4PGS6A22oaY8f8i15MxBgiHhG8vBoNQw0NQJ07I1kFEE2LTRVTg9u+Xz3PVjHYBFrAq98ZFnKI1kEgCibhgEUrBGgPT0S5YBBFNhk0XUYGrrzdwhAfAXxLZBwXJR//Sww9DUlUyAdPaLFoDEU2MTRdRgauvlw/RLw3tzsmhqGcRl/1HAAAgAElEQVTyRZi+tAyB3TtliyCiCbHpIipgIyNAf79CcbFsHUtCuzBqymSLSAOjfRCmLy2Fs68V4veMiegsbLqIClhHh/xQ1JJkGHPivYjrErki0uTUkFRIXjh0HMB1oQ/1CBZBRBNh00VUwPbt00gmZW8tLoy2wYWTk0NRz6QUYC0wGpOuxMJ07JcugojOwKaLqIDt3m3Eby0uGmmBsbk7FPVMFvJhekDBaagXroGIzsSmi6hAWZta/qdMOEp1xfB2xLVw55dGCvLzuryyUjh7dovWQERnY9NFVKD6+xVGR4FAQLAIa3HpSGNehOhPMQYIhYSLKC6BOdQDRKPChRDRW7HpIipQ7e1aPEZ10VgvitwoXB2ULSSNtAFGYwqe5MODWsM6DkxXp2ARRHQmNl1EBaq5WcPzZLuu2mgbLKTzT+mlkArUR6LCQ1ITCZj9baI1ENHp2HQRFajduw3KymSfXHxbuAHK5t88KesBUekhqcEgh6QS+QybLqICFI8DXV0apcLrS18xvB0xUy5bRAZoPwxJLSuD09SQemKCiHxhWk3Xtm3bsGbNGqxevRoPPPDAWd9//PHHceONN2LdunW45ZZb0N7+x8VWf/SjH2H16tVYs2YNXn755fRVTkQzdvCgAmChBX/tcrw4FkbbETPCnV8GGAOEw8JDUoNBqNAw1IkTgkUQ0VtNecp1XRd33XUXHnzwQdTV1eFXv/rVaU0VANx444145pln8PTTT2P9+vXYuHEjAKC9vR11dXWoq6vDgw8+iG9961twuTQFkbj9+zVcV/ZKTM1oF6xSsCr311w8k9ZAIgkkEoJFKAVoDdPOIalEfjFl01VfX4/FixejtrYWwWAQa9euxdatW0/bprz8j7cHRkdHoU4+ErV161asXbsWwWAQtbW1WLx4MerrObCPSNrevQaOI1vDJSP7YEQf8cssBR8MSU26cPa1yNZAROOmPO329/ejpqZm/Ovq6uoJG6ef/vSn2LJlCxKJBB555JHx11511VWnvba/v/+c+zNGoaoq87cbjNFZ2Q9lF4/r9LS2KlRWKtHG64rIHrjGgdLTa0wUAExzW78YHdWYN0+wgMpylDbtRbGPPxP8zOYvHtuzTXnKtROEMNUEw31uvfVW3HrrrXjmmWewefNm3HPPPdN+7Vu5rsXQUOYH+lVVlWZlP5RdPK5TC4eBI0dKMW+eh6Tg6juXDu3CqCqD9aYZfNJq+tv6gNbAwKDFxQs8uSKCxdANjRg6EU4FzXyIn9n8VSjHdt68imlvO+XtxZqaGvT19Y1/3d/fj/nz50+6/dq1a/H888/P6LVElHkdHRpay64vXZoM4aJ4b14t/3MmY4CRESX78KDjANaD7jkoWAQRnTJl07VixQp0d3ejp6cH8XgcdXV1WLly5WnbdHd3j//7iy++iMWLFwMAVq5cibq6OsTjcfT09KC7uxtXXnllen8CIjov+/ZpJJOyV4wWRtrgwpHt/DJMpR4QRWxUuBDPwnS2T70dEWXclLcXHcfBhg0bsH79eriui5tuuglLly7Fpk2bsHz5cqxatQqPPfYYXnvtNTiOg8rKStxzzz0AgKVLl+L666/HDTfcAGMMNmzYAOPTS9xEhWLPHoNi4QtMtZFWGCt4bzOLIlGFklLBJlcpOPV7kVi5Wq4GIgIAKDtR8EpQIuEy00UzxuN6btYCH/94CYqKZBe6vqP177F0eCdGAhdN+zUqxzJdQGoIbXW1xdveLpfrUpEIvIvmIPwfPxGr4Vz4mc1fhXJs05rpIqL8ceyYQjSqRBsuWIu3hxvzchL9mYwBQiHZGmxJCUzPASAWky2EiNh0ERWSzk4NrWWvFs1KHEeJG0ZSSXZ+2aENEB1V8AQfYITWgDEwBw8IFkFEAJsuooLS1qaQTMqG1xdG98NTJq9D9Keok/+MSofpky4n0xP5AJsuogKyZ49BqWSoG0DtSCuMVxgheiC1/GJUeDK9dQycBq4GQiSNTRdRgbAWaGvTKBUeEH15aAfGdIlsEVmkAITCwk1XaSmcJjZdRNLYdBEViN5ehUQCsmsuWovFIy2ImTLBIrLLmNQqAKKKS6D7eoFIRLgQosLGpouoQHR2yn/cLxrrRcCLwdX5H6I/RRsgFlMQXdtbqVSYvrtLsAgikj8LE1FWtLRoeJ50iL4dFvkfoH+rU2H6aFT45066MB0M0xNJYtNFVCD27tXiIfrFI03QkLzkI8MCiArPiLQBB87ePbJFEBU4Nl1EBcB1gY4O44MQ/U6M6cLJc52iFRAKSYfpy+A0N4rWQFTo2HQRFYBDhxSsTYW6pWjrojayDzEj3PkJMEb+CUYUFUGfOA4VGpatg6iAsekiKgCdnRrSq6zOi/VAWxeeknx8UobWqXUYXcnxZEoB2sB0dggWQVTY2HQRFYDmZvmma0G0A6l0U2HyRZjeTXIyPZEgNl1EBWDvXoOyMtmG523hBijpzk+QtfJjsmxREZw9u2SLICpgbLqI8lw8Dhw4oFEiPAR+aWhnQQ1FPZPWwLB0mL6kFE5LM8QvexIVKDZdRHmup0dBKQst+Gk3XgILox0FGaI/xRhgRDpMHwxChcNQAwOydRAVKDZdRHmuo0PDdWX/sq8e7YZVClYJPj4pTGsgkQQSCcEilILVGqazXbAIosLFposozzU2GtFREUBqEr2ynmwRPqAARCOyDbBKJmH2t4nWQFSo2HQR5bmGBvlJ9JeO1AMFtvzPRKwFItKT6UtKEGCYnkgEmy6iPBaLAYcPaxQXy9Zx2fDugg7Rn6INEBqWnkxfCrOvhWF6IgFsuojyWHe3hjGyIfqAN4aaWDfGtPDjkz5gDBAeUbLjygIBqNgY9NF+wSKIChObLqI81tGhkUzKXlmpiXbCgwOreLrRKrUOZjwuXIgCJ9MTCeBZkCiPNTZqOMKr7iyItkNbyfVv/MUfk+ldmLZ9sjUQFSA2XUR5rKlJPkR/WXgvvAIeFXEmX0ymLymBU79HtgiiAsSmiyhPRaNAb68PQvThPQzRv4UxQEh6Mn1pGcy+VobpibKMTRdRnjpwIBWiV4J/vwfcGObFDmFMC3d+PmIMMBIRDtM7DlQiAd3fJ1gEUeFh00WUpzo75SfR14x2wVMOwBD9OOWnMH1Xp3ARRIWFZ0KiPNXQIB+iXxjZzxD9BBimJypMbLqI8lRzs3yI/tLwXnhgiP5MvgnT72WYniib2HQR5SHfhOhH9jJEPwHfhOnbGKYnyiY2XUR56MABDcfxQ4j+MEP0E2CYnqgwsekiykOdnfKT6C8e7YSnNEP0E/BPmF4xTE+URTwbEuUhP4ToF0Taoa0rW4SP+SNMn2SYniiL2HQR5SGG6P2PYXqiwsOmiyjPRKNAXx9D9H7HMD1R4WHTRZRnurs5iT4XGAOMjPgkTN/XK1gEUeFg00WUZ7q6/BKiNwzRn4NSgOv5JEzf3SVcBFFh4BmRKM8wRJ87GKYnKixsuojyDEP0ucPCD2H6UobpibKETRdRHolEOIk+lxjthzB9KcP0RFnCposoj/hnEj1D9NPBMD1RYWHTRZRHGKLPLQzTExUWnhWJ8ogfQvQLI/sZoj8PCkDED2H6fa2yNRAVADZdRHmkqUk+RP/2cD1D9OfBWiDqizD9btkiiAoAmy6iPBGJcBJ9LvLHZPpSmP1tDNMTZRibLqI8wRB9bmKYnqhwsOkiyhMM0ecmhumJCgfPjER5orFRPkR/cbSDIfoZ4GR6osLAposoT/ghRH8ZQ/QzYi0QiQrXUFICp36vbBFEeY5NF1EeGB31xyT6S8N7EDOlskXkIF+E6Us4mZ4o09h0EeUBX4TovTHMj/VgTJfIFZGjfBGmDwSg4nHoo/2CRRDlNzZdRHmgs1M+RF8z2pW6tcgQ/XlTCnBdIJ6QLgTQXQzTE2UKz45EeaCpST5EvyDSDg2G6GfKF2H6ZBKmvU22BqI8Nq2ma9u2bVizZg1Wr16NBx544Kzvb9myBTfccANuvPFG3HbbbTh8+PD495YtW4Z169Zh3bp1uOOOO9JXORGNa2rSKCmRzeK8baQRHn+PmzG/TKYP1O+RLYIoj035u7HrurjrrruwZcsWVFdX4+abb8bKlSuxZMmS8W2WLVuGX/7ylygpKcHPfvYz3Hvvvfj3f/93AEBxcTGefvrpzP0ERAUuFgMOH9aYN0/4ycXQHoxxEv2M6ZNh+gUL5Y6jLS2F2deS6gAlA4JEeWrKX0vr6+uxePFi1NbWIhgMYu3atdi6detp27zvfe9DSUkqPHv11Vejr68vM9US0VkOHtQwxkILXmRyvDhqYgcYor8AvgnTj8agjh8XLIIof015pau/vx81NTXjX1dXV6O+vn7S7Z988klcd91141+PjY3hE5/4BBzHwWc/+1l85CMfOef+jFGoqsr8I+fG6Kzsh7KrEI9rfz+glILjyF2ZWDDSA6sNYAwyVYUCAJ2/V1+MBcaSgIVBUDKfF3RQdfww7NLFWdldIX5mCwWP7dmm/GjbCWa2qEkuOz/99NNobGzEY489Nv5nL7zwAqqrq9HT04PbbrsNl19+ORYtWjTp/lzXYmgo81MCq6pKs7Ifyq5CPK7btwdhrYNk0hOroTrcBngurJfByzRaZfb9/cACoWEPs6rkfk4di2N0dyPGll2dlf0V4me2UBTKsZ03r2La2055Q6Kmpua024X9/f2YP3/+Wdu9+uqruP/++7F582YEg8HxP6+urgYA1NbW4pprrkFzc/O0iyOiqTU2yk+iv3SkHsjYNa7C4YvJ9MXFCNTvli2CKE9N2XStWLEC3d3d6OnpQTweR11dHVauXHnaNs3NzdiwYQM2b96MOXPmjP/58PAw4idXcR0YGMCuXbtOC+AT0YUZGwMOHfLBJPrQXsQ0byNcKG2AsPRk+reG6Ykoraa8veg4DjZs2ID169fDdV3cdNNNWLp0KTZt2oTly5dj1apV+Pa3v41oNIovfelLAICLL74Y999/Pzo6OvCNb3wDSilYa3H77bez6SJKo54eBa1lQ/TGS6BmtBvDgblyReQJo0+G6SUFAlBDQ1ADA7Bv+SWaiC7ctOKaH/zgB/HBD37wtD871WABwMMPPzzh697znvfgmWeemXl1RHROXV0arqsg+chbdewArFKwnER/wbQGxuJAIgEEAkJFKAVoBdPVgSSbLqK04lmSKIc1NWnRq1wAsCDaAWXlQvz5RgEYFZ9M78J0tMvWQJSH2HQR5bDGRiMeol8cbpKdLZVnrAUi0pPpi4vh7OVkeqJ0Y9NFlKPi8dRg1BLheaSXhXcjZhiiTxetgVBYPkzvtDJMT5RubLqIctShQ/Iheu0lsTDagTE2XWkzPpleUiAAFQ5BDQ3K1kGUZ9h0EeWori4Nz5P9y3l+7CAsFKwyonXkE61TQfpkQrAIpQBjYLo6BYsgyj9suohyVHOzFl+TeMFoJxQDXWmnAERHpcP0SZiODtkaiPIMmy6iHNXQ4I8QPZ9cTD9rgah4mL4IDifTE6UVmy6iHJRMAgcOyIfol4R3IWbKZIvIQ74I05eUwmlpEa2BKN+w6SLKQT09KjXDUjxE384nFzPAGGBEuOlCMAgVGmKYniiN2HQR5aCuLi3+NP+8sUOAtQzRZ4DWQDwBuEnBIpQCjAPT3SVYBFF+YdNFlINaWuSbrgXRDoboM0gBiEpPpk8kOJmeKI3YdBHlIF9Moh9pgpLu/PKYBRCJCtdQVMTJ9ERpxKaLKMckk6nbi+Ih+tAehugzSCsgHJKeTF8Cs49heqJ0YdNFlGMOHUr9RWwEo1TaulgYbcOYEe788pgxQFh6Mn2wCHpgACo0LFsHUZ5g00WUY7q75fNcc2OHoK0HTzmyheQxrVPra8qH6TmZnihd2HQR5ZjWVvmma0G0gxH6LFDKB2H6ZBKmk5PpidKBTRdRjvHDJPpFIy3Q1hWtoRBYC0RHhWsIBmEa6mWLIMoTbLqIcojrAp2d8iH6peFdGGOIPuO0AkLiYfpSOM1NojUQ5Qs2XUQ55PBhBWtlQ/TKelgYaeMk+izwxWT6oiLoE8ehRsKydRDlATZdRDkkFaKXvbU4d+wwjHUZos8CrYExn4TpdRcn0xNdKDZdRDkkFaKXvfLBEH12KQVER4WvdrkM0xOlA5suohxSX69RUiLb8tSOtMAwRJ811gKj0pPpAwE49ZxMT3Sh2HQR5QjXBTo6jHiIfkloF/NcWaQUEBLOddnSMobpidKATRdRjujtVfA8wJGMUlmLRdF9iGk+uZgtqTC9cBFFRdDHjwIjI8KFEOU2Nl1EOaKrS0Mp2VuLc8aOwHhJeJoh+mwxGoiNKbiSd3SVAhwHpptheqILwaaLKEe0tmq4LkP0hUgpYNQPk+m7GKYnuhBsuohyRH29lp9EH2GIXoK1QNQXYfq9skUQ5Tg2XUQ5wPP8EqLfzRC9AKWAsHiYvhROC8P0RBeCTRdRDujtVXBdKx+ij7RgTLPpyjajgbB4mL4Yur8fiESECyHKXWy6iHJAKkQvW8NFY70IeHG4OiBbSAHSJhWm98TD9IZheqILwKaLKAe0tWkkk8Ih+tFOeNKdX4FSOBmml55Mn3RhujtlayDKYWy6iHJAQ4N8iL420grjSS4CWNisBSLSYXrHgdNQL1sEUQ5j00Xkc9amrnSVCkeplg7vxJjhUFQpvgnTNzeK1kCUy9h0Eflcb69CMumDSfSRVsQYohfjizB9cTF0by/D9EQzxKaLyOd8EaKP9yHgxRiiF6QNEIvJh+mt48Ac6BYsgih3seki8rl9+3wQoo+0IxXnJimnwvRR4TC94mR6ohlj00Xkc/X1GmVl8pPoNSfRi7MWiArf2bOOw8n0RDPEpovIxzwPaGvzxyT6MU6iF+ebMH1Tg2gNRLmKTReRj/llEv3iSDND9D7gmzA9J9MTzQibLiIf80WIfqwXDifR+wIn0xPlNjZdRD7W2qrhusIh+mgHLEP0vjAepo8KH48Ew/REM8Gmi8jH/DCJflGkBYYhet+wFohKT6YPBhimJ5oBNl1EPuV5wP798iH6paGdiDFE7xv+CdNzMj3R+WLTReRTvb0Knicfol8UacUYQ/S+YYwPwvRFxdBH+4GREeFCiHILmy4in+rslP94zhk7AsdLMETvI0anwvQuw/REOUf+rE5EE/JPiJ78RilglGF6opzDpovIp+rr/RGi12CI3m/8E6bfI1sEUY5h00XkQ64LdHT4IEQ/vJN5Lh9SCgj5IkzfJFoDUa5h00XkQ0eOKHge5EP00VbEdJlgETQR34Tpjx2FGpEuhCh3sOki8qGuLg0Ip6lOheg9Ldn50USMBsbGFNykYBFKAcZAdzFMTzRdbLqIfMgPIfqF0XZOovcxpYDoqPDxSSZhOhmmJ5ouNl1EPuSLEP1IMzQn0fuWtcAow/REOYVNF5HPnArRlwrn15eEdnESvY/5I0xfBqeZYXqi6ZpW07Vt2zasWbMGq1evxgMPPHDW97ds2YIbbrgBN954I2677TYcPnx4/HtPPfUUPvrRj+KjH/0onnrqqfRVTpSnDh9WsDYVlhZjLRZF9jFE72PGAOGQ8O3FoiLo4wzTE03XlE2X67q466678OCDD6Kurg6/+tWv0N7efto2y5Ytwy9/+Us888wzWLNmDe69914AwNDQEH74wx/iiSeewC9+8Qv88Ic/xPDwcGZ+EqI80d0tH6KfO3YYxiYZovcxo4GxOHwQpncYpieapimbrvr6eixevBi1tbUIBoNYu3Yttm7deto273vf+1BycqDQ1Vdfjb6+PgDAK6+8gg984AOoqqrCrFmz8IEPfAAvv/xyBn4MovzR0qLheX4I0ZPf+SZM39E+9XZEhCl/je3v70dNTc3419XV1aivr590+yeffBLXXXfdpK/t7+8/5/6MUaiqynyOxBidlf1QduXDcW1pUaioUKIzut4ebYaBB6X98/SiAgAf1eML1mJsTOOi2YI1lBajvLUBXtX/mNHL8+EzSxPjsT3blKd1a8/+fVepiU98Tz/9NBobG/HYY4+d92tPcV2LoaHMP5JTVVWalf1QduX6cU0mgdbWUsye7SEpeNvosqEdiOlSWM9H17u08lc9PqAUMDhgMXeuJ1dEUTGwew9CM/zc5fpnliZXKMd23ryKaW875e3Fmpqa8duFQOrq1fz588/a7tVXX8X999+PzZs3IxgMntdriSilpyf1S4lkiF5bF5dE2hAzDNH7nTFAeET46l+wCHpgAGp4SLYOohwwZdO1YsUKdHd3o6enB/F4HHV1dVi5cuVp2zQ3N2PDhg3YvHkz5syZM/7n1157LV555RUMDw9jeHgYr7zyCq699tr0/xREeaKrS8MTvGgBAPNGD0JZC09JPj5J06E1EI8DyYRgEScn03NIKtHUpry96DgONmzYgPXr18N1Xdx0001YunQpNm3ahOXLl2PVqlX49re/jWg0ii996UsAgIsvvhj3338/qqqqcOedd+Lmm28GAHz+859HVVVVZn8iohzW0KAxxR34jFsYbYeCcOdH06YARKMKlbMEb70mkzD725B895/I1UCUA5SdKHglKJFwmemiGcv147p+fTFOnFAoE7yzd1PX93Bd3y8wVFQtV8QEFDNdExqLA4sXebh4gdx/GzU0iOSKqxHZeO95vzbXP7M0uUI5tmnNdBFRdoyNAQcPapycviJmaXgn81w5RGsgJDwk1ZaWwWltTq1NRESTYtNF5BMHDmgYY6EFP5WOF8eCaCfGuPxPzjAGCIeV7DzdQAAqEoE6flywCCL/Y9NF5BOdnQrJpOwVi5rRLnhKwyqeGnKFVkDSBRLSYXqtGaYnmgLPrEQ+0dBgRAeiAsDCyH5o68oWQedNAYhEhZ/AcJMwba2yNRD5HJsuIp9oaNAoLZXNxFwarofH00LOsRaIRIRrKClBYM9u2SKIfI5nVyIfiESAvj6N4mLZOpaEdyFmymWLoPNmDBAalg/Tm7ZWhumJzoFNF5EPdHenQvSSM7qC7ijmxQ5hTAt3fnTejAFGRoTD9I4DFY9D9/UKFkHkb2y6iHygs1OLrrUIABdHO+DBAAzR5xylAM9LzeySZjrapUsg8i2eXYl8YO9ejUBA9vbQwuh+aDBEn8ui0mF6z4VpaZatgcjH2HQR+UBTkxEP0S8N7YI79cpg5FMWwEhYPtfl1O8VrYHIz9h0EQkLhYATJxSKimTruCy8l5Poc5gxqf+XJNnSUjjtbYDLK6ZEE2HTRSSsq0tDa4iG6EuSYcyO9yPOEH3OMhqIRITD9MYAngd9qEewCCL/YtNFJGz/fg3Xlb21uDC6H65yZDs/uiBKpaY1xGLSlVhOpieaBJsuImF79mjxW4uXRNrgeJLryFC6iE+mt4DT3ChbA5FPsekiEmQt0NJiUCq8vvTS0E7eWswDvgjTl5XCqd8jWgORX7HpIhI0MKAQDisEArJ1XBpuYIg+D/giTF9SCtPdBcR9MDSMyGfYdBEJ6uxU0Fp2En1FYgDlyUEkVFCuCEoLY1KzuqwnWITWgNIwBw8IFkHkT2y6iATt2yc/iX5hpA0uDEP0eeDUERwdFS0DcF2Y9v3CRRD5D5suIkG7d8vnuRaPNMOxwp0fpY3FydERkjU4Bk4Dh6QSnYlNF5EQzwNaW7V403XF8HbmufKIUkBIOkxfWsYwPdEE2HQRCTlyRCGZVHAEV95R1sPbIk2IaeHOj9LGGCAsHKZHcTF0Xx/USFi4ECJ/YdNFJKS9XUN2fDgwN3YIxkvC1cKPT1LaGA3ExhRcyTvGSgHGwHS0CxZB5D9suoiENDVpWCt7G6g22gbpxo/ST8EHQ1LdJExbm2wNRD7DpotIyO7dBmVlsg3P20P17LnykAUQGRGuoagIzp6dskUQ+QybLiIBsRhw8KBGSYlsHZeHdjBEn4e0BoZDPgjTNzWkll0gIgBsuohEdHdrGGOhBT+BAW8MF492YcwwRJ9vjDm5HJBkvxMMQo2OQh/tFyyCyF/YdBEJ6OjQcF3ZGhZE2+EpDat4Gsg3WgGuC4xJr8SjNMP0RG/Bsy2RgN27NRxH9vbPwkgbtBXu/CijouJhehemuUm2BiIfYdNFJKChwaC0VDbrcsXwDrgQHBJGGWUBhMWHpJbC2c0wPdEpbLqIsmxwEBgcVCgqkq3jsvBejJpy2SIoY4wBQsOyNdjSUjgd7RBfYJTIJ9h0EWVZZ6eGEV5fujwxiFmJY0ho4c6PMsaY1Kwu6wkXAcAc7BYsgsg/2HQRZdm+fRqJhOytxYWRttStRcnOjzJKAYAFRkeFC/FcmHaG6YkANl1EWbdzpxGfz7V4pBnG8pZPvrOQn0xvtYbZy8WviQA2XURZZW3qSleZ8DzSK0LbMaaFOz/KOAUgLD0ktawMgQY2XUQAmy6irDpyRCGZBBzJhwatxdtGmjiJvgAYBwiFhIsoLoHu7QVGhNclIvIBNl1EWdTersVXRZk3dgiOF4erA7KFUMYZDcTGFFzJO8lKAcbA6eoQLILIH9h0EWVRY6OGtbK3ey6JtInun7JLwQdDUpNJmH2tsjUQ+QCbLqIs2rvXoKxM9lLX28P1UNKX2yhrrAUiEeEaiovg7NklWwSRD7DpIsqSsTHgwAEt/uTiFcPbORS1gGgDDA9LT6Yvg9PcBPF760TC2HQRZUlXl4ZSFlrwUxdwY7h4tAtjplSuCMoqY04uByTZ7wQCUCNhqKNHBYsgksemiyhL9u/XcIXXl14QbYcHDav40S8UWgGuC4zFBYtQClAaTjvzhFTYeOYlypKdOzWCQdkaFkVaORS1QEUiwmF668Fp2CtbA5EwNl1EWWAtUF9vxIeiLht6nestFiALIOSDIanOrh2iNRBJY9NFlAVHjyqMjCgEJEdjWYvLwnsYoi9AjgGGh2VrsCWlMN3dQCwmWwiRIDZdRFmwf38qRC+5vvRFY70odiNIauF7nJR12gCjowqeZCwaypsAACAASURBVKZQa1ijYTq4+DUVLjZdRFngh6GotZF9ovsnOerkP9K5LpVMwmnjkFQqXGy6iLJg1y75oaiXhfdwKGoBsxYYkR6SGiyCs3O7bBFEgth0EWVYLAZ0d8sPRX3H8BvMcxUwbYDhIeEwfXkZnMZ6DkmlgsWmiyjDOjo0jJEdihp0R1Ez2o0Yh6IWLMcXQ1KDUKMx6L5ewSKI5LDpIsqwtjaNpPBorEuibfCUATgUtWApBbgeEBuTLgQw+zkklQoTz8BEGbZzp0aR8GisxeEmGI9DUQudAhAZER6S6lkOSaWCxaaLKIOsBRob5YeivmP4TcR1sWwR5AuhMIekEklh00WUQX19CqOjEB+Kemm4niF6gjFASHxIaglMTw8QEX6UkkjAtJqubdu2Yc2aNVi9ejUeeOCBs76/fft2fPzjH8c73/lOPPvss6d9b9myZVi3bh3WrVuHO+64Iz1VE+WI/fvlf6+ZN3YIQS8GV0t2fuQHxgCjMQVX8k6z1oDWMJ0dgkUQyXCm2sB1Xdx1113YsmULqqurcfPNN2PlypVYsmTJ+DYXX3wxNm7ciIceeuis1xcXF+Ppp59Ob9VEOaKh4dRQVLlHxlJDUfmIPqUoAJGoQmWl4P8TySSc1ha4K66Uq4FIwJS/htfX12Px4sWora1FMBjE2rVrsXXr1tO2ueSSS/COd7wDWvKZeCIf8sVQ1NBu0f2Tv1gLjIwI11BcjMCuN2WLIBIw5ZWu/v5+1NTUjH9dXV2N+vr6ae9gbGwMn/jEJ+A4Dj772c/iIx/5yDm3N0ahqirzs4SM0VnZD2WXn45rJAIcOaIxbx5EZ3QtC21HzKmA0sJPrV0gBQA5/jP4gXGAcEgjsEiwiFmVcFpb4Mwq8dVnltKLx/ZsUzZddoLJweo8Vu194YUXUF1djZ6eHtx22224/PLLsWjR5J9217UYGopO+/1nqqqqNCv7oezy03FN3VosgucBnidTQ1EygrmjBzAYqAG8HL/FqBVsrv8MPmA0EAoBiYR7spMVoDR0dBShpjZULr/CN59ZSi8/nY8zad68imlvO+Xv3zU1Nejr6xv/ur+/H/Pnz5/2DqqrqwEAtbW1uOaaa9Dc3Dzt1xLlsn37NFxXtoba6D54yklNxiRC6n8Fz0stTyXLckgqFZwpm64VK1agu7sbPT09iMfjqKurw8qVK6f15sPDw4jH4wCAgYEB7Nq167QAPlE+27nTcCgq+dZIRLoRV3D2Mm9IhWXK24uO42DDhg1Yv349XNfFTTfdhKVLl2LTpk1Yvnw5Vq1ahfr6enzhC19AKBTCCy+8gB/84Aeoq6tDR0cHvvGNb0ApBWstbr/9djZdVBBSQ1G1+FDUZcNvYEwLr7RNvhQaVpg7V+52rVdWhsCunWL7J5Kg7EShLUGJhMtMF82YX47rwYMKt99egnnz5D5e2rr4zpsfRsRUwtNT/n7le4qZrrRxvVS2693vEbz/bS300aPA889jyHKGXD7yy/k409Ka6SKi89faqsXC86fURDuhkcyLhovSy2hgLA4kEoJFKAUYDdXCnC8VDjZdRBmwY4eBI9zrLB5phrbCnR/5lgIwIr34tesCe7j4NRUONl1EaWYtsHu3QXm57K2wZcOvI6l424YmZgGEQ8KLX5eWQb32qmgN/3979x4nVX3n+f/1PZeqvnfTXBpUxKhEk2DUxNlxozE7ZJAxTMYkknnM75Gd30523WQe0bhOVjPuJEMmZHLbmFWzZhKNScx1TDBBRxhFgiKiCMjF5qLcFLl1N03fu/pSVed8949q8C4NVNe3uur9fDz6AQ0F/YZTXedT5/s5n69IIanoEsmz9nZDT48hkXAYwlrO63lWm1zLWwp86O52m8FWV2N27SyG+RUiBaGiSyTPduzwMMY6HY01cfgQFVE/Wc/xzAopWp4PA4NFsPm18TSvS8qGii6RPNu06egm1+7M6Fdzsrw9wyubXzsVZQme3+Y2g0iBqOgSybMNG9z3c72zZz3W2R4vMl5YC319jkNUVBKue8ZxCJHCUNElkke9vdDS4lHpeB7pu3rWMuiPfnaMlCffh+4ux8V5TQ3Btq2Q1c4JUvpUdInk0Y4dXq5NxeF5rDbTyYR0G2mvwl0IGRf8IDc2wulkkSAAG+O//JLDECKFoaJLJI+2bPGdb3I9o387Mb42uZbjOvoMGXDe1xXhv/C82wwiBaCiSySP1q/3qa522891bu9GPOu48pNxw1ro63ecIUwQrlvrNoRIAajoEsmToSHYs8ejqsptjnd1r9F8Lhk1z4fubsdDUmtqCJ7bmKsARUqYii6RPNm1y8PzLJ7D76pkNsW0wZcY8h1XfjJuBP7IZHqX9U4igRkcwjt00GEIkbGnokskT7Zv95zfgHVm6vmRfi59a8voGANxDIODjoNYq74uKXl6ZRbJk3XrfOejIs7ua8ZHt97LibG43/zaeoZg4wanGUTGmooukTzIZmH7dp/qarc53t29hiFPS4tyYoyBnh7XfV21hBvXO80gMtZUdInkwd69BmtzI4dcCeI0M1Lb1UQvJyzwR4oul31dySReRwemo8NhCJGxpaJLJA927HA/n+v0gV0AWOO7DSLjjudBJgvDaYchjAHPEOx8wWEIkbGlokskD9au9QhDt7e7n9W3FT/WfC45OQZIOe7rIo4JntvkNoPIGFLRJXKK4hg2b/apdbzV4ayu1aS9pNsQMq51O+7rimtqCZ5Z4zSDyFhS0SVyil5+2TA8bAhDdxm8OMu5fZsYCLTJtZycIHA/JJXKSvyDBzDdXW5ziIwRFV0ip2j7dt/5IO0zBnZirCU2Djv5ZVzzPEinIT3sMIQx4HkEz293GEJk7KjoEjlFa9a47+c6p3ez5nNJXvT1Ob7aZWOCjc+6zSAyRlR0iZyCKMr1c9U4ntJwQfdqho3jyawy7hmKo68rVF+XlCgVXSKnYO9eQybjtp8riNOc3fccg4Hmc8mpOdbX5fLCbWUlXmsLplPzuqT0qOgSOQXbtvnEsdsMp6d2AqifS06Z50EmUwzzujyC7dschhAZGyq6RE7BmjU+yaTbfq5z+zbjx+rnkvxx3teFJdigLYGk9KjoEjlJ2Sw0N3vu+7m6nmTY136Lkh/GuB8dEdfUEq5VX5eUHhVdIifppZc8osj9fotn9W1lQPstSp4EPvS47uuqqMA70o5pb3cYQiT/VHSJnKStWz3n+y2emXoejPZblPw5ug/jkOt5XcYjeF59XVJaVHSJnKRcP5fbDOf0bsJTP5eMgX7nfV0QrF/nOoJIXqnoEjkJmQxs2eJ+Ptd71c8lY8AY6HLd11VbS7huDc63exDJIxVdIidhzx4Pa63Tfq4wGuLM/ucZVD+X5FkQFEFfVzKJ19mJd7jNYQiR/FLRJXIStm71yDpe1ZuR2o7FqJ9L8s4zud0WhoYchjAGjMHXvC4pISq6RE7CmjU+lY533dF+izKWLO7ndVnPEK5f6zSDSD6p6BI5Qek0bN9eDP1cTzDkVbsNISXLK4K+Lltbmyu61NclJUJFl8gJ2r3bAyy+w1W9ZDTAGamd6ueSMRME0NPjuK8rkcT09OC1tjgMIZI/KrpETtCWLUXQz9W/DYuHNfoWlrFhDMQRDA46DoEl2LbVYQiR/NErtsgJevLJgCrHUxrO616Pbx1PZpWSZ4Fe131dQUCw5imnGUTyRUWXyAno74edOw3VjlupLup6nIGg1m0IKXmeB50dRdDXte4ZnG//IJIHKrpETsD27R6eZ/AcfufUpY8weWg/Q56GosrYCgLo7TXEscMQYQKTSeO/uMdhCJH8UNElcgLWr/fdnoCAc/s2YzEj/S4iY+foM6y/3/FzLRsRbN7oNoNIHqjoEhkla2H16oC6Ore3r1/QuQqrb10pEMvIXYwuM1RVET75hNMMIvmgV26RUWprM3R2GqebXBsb857up0gF9e5CSFkJfOjsdJvB1tQQvPA8pFJug4icIhVdIqPU3OxhrdtVvWkDe0hGg2S9hLsQUlZ8HwYHDZmMwxCeB55HsF2jI2R8U9ElMkqrV/sEgdulxZm9G/A0KkIKzAB9vY77uuJIWwLJuKeiS2QUslnYsMGnrs5tjos6H2fY112LUnhdXW6Lrri2jvCp1U4ziJwqFV0io7B7t0cU5W6hdyURDXJ23xYGtPWPFFgQjBRdLi/0VlTgtbfhtbU6DCFyalR0iYzCpk2e89mMZ/VvxWKwxuGmj1KWPC83m9T9lkAQND/nMITIqVHRJTIKxbD1z7u6n8HD8aaPUrYsuUGpTjMEIeFTTzrNIHIqVHSJHEd/P+zeXQRb/3Q+zoCvURHihu9BR6f7LYGCDetxvuO8yElS0SVyHFu3+kfvWHemfvgwE4cPMexVugshZc0Pcncwxi6X2cMwtyXQnt0OQ4icPBVdIsexdq1HHLt9h39u7yZt/SNOFc2WQFFEsGmD2wwiJ2lURdeqVauYO3cuc+bM4e67737D769fv56Pf/zjvPvd7+aRRx55ze8tXryYK6+8kiuvvJLFixfnJ7VIgVgLTz9dBFv/dK0iRg304pYFup1vCVRN+OQqpxlETtZxi64oili4cCH33HMPS5cuZcmSJeze/dpLu9OmTeOb3/wmf/7nf/6aX+/u7ubOO+/kt7/9LYsWLeLOO++kp6cnv/8CkTHU0mLo6qIItv5ZQypwPCRMyl5RbAlUXU2wawemv89tEJGTcNyiq7m5mRkzZjB9+nQSiQTz5s1jxYoVr3nMGWecwfnnn4/3uqaX1atXc9lll9HQ0EB9fT2XXXYZTz6pO09k/Ni82cda43RV74zUDhLxEJG2/hHHfB+GhgzpYYchPA88Q7Cl2WEIkZNz3FGPbW1tTJ069djnTU1NNDeP7sn+Zn+2ra3tbf+M7xsaGsb+3nzf9wrydaSw8n1cn3rKUF1tnA5FndW7Dg+L8cq7n8sAlPn/QTEwxpJK+Xm7m9cYQxic4NK551G74Rniq+bkJ4SMCZ1n3+i4pxJr39jLYkb5tv9k/mwUWbq7B0b195+KhoaqgnwdKax8HtdUCp59tpLGRrd3qF/c/jADXjU2dttX5pxn9H9QBAzQ1maZ0Bjn5e8LA59M9gRviaypxfzhD/R87u/c3lYsb6tczrOTJ9eO+rHHfbZOnTqV1tZXtl1oa2tjypQpo/rLT+XPiri2ZYsPGHyH/ev1w4eZOriXId/xkDCREUEIPa5HRyQSmMEh/F07HYYQOXHHLbouuOAC9u7dy/79+0mn0yxdupTZs2eP6i+//PLLWb16NT09PfT09LB69Wouv/zyUw4tUghPPeX+bsHzetbnfqJREVIkDICFvj7Hz8k4Ilj3jNsMIifouEVXEAQsWLCAa6+9lo985CNcddVVzJw5kzvuuONYQ31zczNXXHEFjzzyCF/5yleYN28eAA0NDXzuc59j/vz5zJ8/n+uuu46Ghoax/ReJ5EEcw5NP+tTXu13Oen/HcjJGDfRSXCzQ6Xo6fU0Nicf/4DSDyIky9s0arxzKZCL1dMlJy9dx3bnT4/Ofr2DyZHffHmE0xHfWf5i+sJFYm1xj1NNVNOKRdq73vz96ZWrqSTqpni4Aa/HaD9Pzy0VYta0UpXI5z+a1p0ukHOWm0Ls9wZ/TtxmwKrik6Hhe7uaSAZfn05El93Djsw5DiJwYFV0ib2LlyoCaGrcZLux6Aq+4LkSLHGOB7m7HS4yJBImVK477OJFioaJL5HXa2w3793tUuRwvYy0XdTxOf1jvMITIW/N9ONLhuOiqrSPYvBEGB53mEBktFV0ir7NxY+7bwuUNg6cN7KYq20vGq3AXQuRtBD4MDBgyaYchRua5aDq9jBcqukReZ+XKgETC7bLeu7vXYMjP8EmRsWLIzexyKraET2t7ORkfVHSJvMrQEGza5FM7+ptRxsT7Ox5lyNNAVCluxkCH4yXGuL6OxKqVoP5HGQdUdIm8ytatuW8Jl1Poa9MdnD6wm0HfcSe/yHEEAfR0m2MjJJxIVmBSKfwXdzsMITI6KrpEXuWpp3y3JxByU+gtRlPopegZk7vA1O98On1MsG6t2wwio6CiS2SEtbBqVeB8Cv3FnSuIzHH3ohcpChbo6nJ8F2N1FYnHNTpCip+KLpERe/YY+vsNyaS7DGE0xLu7nyEVaFSEjA9hAEeOmFz15YitrsF/6UVMe7u7ECKjoKJLZMTq1b7zKfTv7H0WYyNiXemSceLodPpUyuHVLmMAS7hujbsMIqOgokuE3NLio4+G1NW5zXHJkWWc8mZ2IgVWFBtgV1aSWPaw0wwix6OiSwR46SVDR4ehwuEs0iBOc2HnE/QHDe5CiJyEIMjt5OB0ibGmluCF7ZjODnchRI5DRZcIR+9atE5vGDy3dyO+zRJ5obsQIifB9yCTcbwBtueBhVB3MUoRU9ElZc9aWLYsdD8Q9cijbgOInAILdLq+i7EiSeJRLTFK8VLRJWVv3z7D4cOGykp3Gfw4w/s6H6MvmOAuhMgpCHxoP+y5XWKsrSPYugXT3eUuhMjbUNElZe/pp32sdbu0eE7fZvw4raVFGbd8H4bTMDjoMITnAZbw2fUOQ4i8NRVdUvaWLQuocbzjzvuO/AGjveOkBDhfYkwktMQoRUtFl5S1gwcNhw4ZqqrcZfDiLO/vWE5/qKVFGd9yS4yO72Ksqyd4bjOmr9ddCJG3oKJLytqaNbmdrV0uLZ7d30xoh8h6CXchRPLA92Fo2DA05DDEyBJjsEFLjFJ8VHRJWXvkkcDpVS6AiztWaGlRSorzJcYgJPHoMqcZRN6Mii4pWy0thn37PKqr3WXwbMQlR5ZpIKqUDP/oEqNDtq6OcMN66O93mkPk9VR0Sdl65hkfa90uLc7o30ZFNEDWc7jLtkgeBT4MDjleYvR9sDZXeIkUERVdUrZyS4tul/Uu7liBIXaaQWQsdDlfYvRJ/EFLjFJcVHRJWTp40PDSS57TURFenOXS9qX0ayCqlBjfh7Y2x3cx1jcQrluruxilqKjokrK0cqX7gagzezeQjFJktLQoJSbwYWjIuN2L0ffBxoTPrHEYQuS1VHRJ2bEWlixxv9fif2x/CIvbJRiRsXSkw+0pxiYrSPzbYqcZRF5NRZeUnRde8OjsdDsQNZlNcVHn4/SFje5CiIyhMIDDbQaX01BsXR3BjufxWlvchRB5FRVdUnaWL/ednggAZnWtxrMxsQncBhEZI54HUQR9vQ6v5ppcX1m48jF3GUReRUWXlJV0GpYvD2hocFt1XX54MRmjCfRS+trb3S6hx7U1JJc8iPN3WiKo6JIys3GjTzptSDisd+qHD3NO32b6g3p3IUQKIAyho8MQRw5DVFbhtR/G37XTYQiRHBVdUlYeftjPbc3m0EWdI0sdRt9+UtqMgdhCV7f7JcbE8kfcZRAZoVd9KRu9vbB2re92adFarmi9n0HP4YAwkQLyTK6h3qW4vp7Eo49AJuM0h4iKLikbzzzjE8cG33eX4bSB3UwePsiQ73DDR5ECCkLo6TVk0g5DJJOYoUGCzRsdhhBR0SVl5N/+LaSiwm0z7SVHlgGON3wUKaCjz/TOTsfbAnkeyUeWOs0goqJLykJLi2HnTs/pQFQvzvKBww/SHzS4CyHigO9DazFsC/T0U5j+PnchpOyp6JKy8MQTPuB2259z+zZRGaXIeBXuQog4EPgwOGgYHHQZIoAo0rZA4pSKLil5UQQPPBA63dwa4LK2xS7f6Is4197ueFugiiTJ3y9ymkHKm4ouKXnPPefR2YnTbX9qM51c1LmS3nCiuxAiDoUhtLW5ndlla+vwd+/Ce3GPuxBS1lR0Scl74IEA33fbxPtH7Q9jiLHG4a2TIg55BqIYurocz+wCkv++xF0GKWsquqSktbcb1q4NmDDB3cKeZyNmt/wrA36dswwixcD3cje1uBRPmEDi4SWQSjnNIeVJRZeUtEcf9bHWOp1CP7N3A7WZDoZ9h+ubIkUgCKA/ZRgYcBgiDDGZNImnVzsMIeVKRZeUrGwWFi8OqXN8gemKlkXEaFlR5KjDhx031FdWkvztv2oTbCk4FV1SsjZu9OjrM1Q4nNBQP3yYWd2r6QsnuAshUkQSYW5boMhlQ31NLf7LL+Hv3uUuhJQlFV1Ssn73uxDfd/tO9o+P/DsGqwZ6kRHHNsF2OaHeGDCGxEMPuMsgZUlFl5SktjbDpk0+DQ6Hv3txlj9puY+UX+8uhEgR8j04dMjthPp4QiPJPzyqCfVSUCq6pCQ9/LD7Bvrze9dRle0h7Ve6CyFShIIABgYNKZcN9UEA2QzhEysdhpByo6JLSk4mAw8+GDq9ygXwn1ruIzKB2xAiRaytzXFDfVUVFYvuU0O9FIyKLik569b5pFKGZNJdhsahQ5zfs47+QA30Im8mEcKRdkPWZUN9dQ3ewQP427e5CyFlRUWXlBRr4Ve/Ckkk3L5z/VDrIrAGa/QtJvJmjjbUH25zHML3cle7RApAZwQpKVu2wK5dHvUOe9ersr1c0XY/vWGjuxAi40AQwP79EMfuMsSNEwmffhLv4AF3IaRsqOiSkvLznxt8/9gWa05cevghfJsm8kJ3IUTGAd+DTNa4HR/heWAh+ftF7jJI2VDRJSVj/37DU08ZGhvdLS0GcZq5B+8l5Tvu4hcZJ3wP9h9wPD6isZHkvz+E6e5yF0LKwqiKrlWrVjF37lzmzJnD3Xff/YbfT6fT3HjjjcyZM4dPfvKTHDiQu0x74MAB3vve93L11Vdz9dVXs2DBgvymF3mV++/P3SnockzERR0rqIz6NCZCZJSCAIaGDD29Dq92hSFEEYmlD7nLIGXhuPezR1HEwoUL+elPf0pTUxPz589n9uzZnHvuucces2jRIurq6li+fDlLly7l1ltv5fbbbwfgzDPP5MEHHxy7f4EI0NkJy5YFTJrkLoOxMfMO/Ighr9pdCJHxxoBn4OABQ329u8tdcX09Fb/9V4av+Uuc7h0mJe241wSam5uZMWMG06dPJ5FIMG/ePFasWPGaxzz22GN8/OMfB2Du3LmsWbMGq7knUkBLlgTEce5dsyvn96xl4vAhBv0adyFExqEwhN4+QyrlMESyAjOQIvH4HxyGkFJ33FNUW1sbU6dOPfZ5U1MTzc3Nb3jMtGnTcn9hEFBbW0tXV25t/MCBA3zsYx+jpqaGG2+8kUsuueRtv57vGxoaqk74H3KifN8ryNeRsTcwAA88YJg40WAMBIGbfQ4/cuinZL0kxler5FgwkLskIiXl6HH1DLS2+px/nsMw9fXU3vdLor+8Bnztl3qqdJ59o+MWXW92xcq87tawt3rMlClTePzxx5kwYQJbt27luuuuY+nSpdTUvPWVgCiydHeP/d4QDQ1VBfk6MvYeeiigry9BVVWMtT5ZB9MWp/c/z4zeLXSFU3LDhyT/PIPV/23pGTmuQQjt7XDGGZG7wcYVlXgtraSWrSDzgcsdhSgd5XKenTy5dtSPPe5b8qlTp9La2nrs87a2NqZMmfKGx7S0tACQzWbp6+ujoaGBRCLBhAm5idyzZs3izDPP5KWXXhp1OJHjyWZzw1Bra92ejK88+DNijNtZFSLj2NHvnNYWx1sDJRNU/OJebQ0kY+K4z+4LLriAvXv3sn//ftLpNEuXLmX27Nmveczs2bNZvHgxAMuWLePSSy/FGENnZydRlLvqsH//fvbu3cv06dPH4J8h5Wr1ap+ODkOVwyvYUwZf5sKulfSGE92FECkBiRDa2gyZjLsMtq4ef/cugi3PuQshJeu4y4tBELBgwQKuvfZaoijimmuuYebMmdxxxx3MmjWLD3/4w8yfP5+bb76ZOXPmUF9fz2233QbA+vXr+d73vofv+/i+z1e/+lUaXO9CLCUjk4G7705QXe32Hem8/Xdj8bBGPSAip+Lo1kCHDnnMmOFoTL0x2ESCyh/9kL7v/UBXryWvjC2y2wwzmUg9XTIqjz7q853vJGlqeuUpHASF7ek6LbWLW7b8Nd3hFO2zOMaMerpK0uuPq7WQycLFF0UkXPV2WYt3uI3+b9xK9o/+g6MQ41+5nGfz2tMlUoyGh+GeexLOe7k+uv+HxHgquETyxJhc4XXwkMPvKWOwFZVU3v0v6u2SvNKZQsalRx8N6OrCaS/Xmf3bmdW1mp7Q4URWkRKUCOFwm2F4yF0GW1eHv/dFwjVPuQshJUdFl4w7g4Pwk5+E1NU5DGEtV++7k6wJQVe5RPLKjGzFeOCA46tdVVVU3vV9iAo/hkZKk84WMu4sWRLQ12eodLi94dl9zzGzd6PuWBQZI4kEtB8xDA66y2BravEOHSR8cqW7EFJSVHTJuNLfD7/4RciECQ77LKzl4y9/j6xJ6M4mkTFiRj4O7Hd8tau6hsq7f4DTORZSMlR0ybjywAMBg4PG3cRq4LyedZyV2kZfMMFdCJEyECago9Mw4HBPRltTg9feTmLFo+5CSMlQ0SXjRlcX3Hef26tcno34xMt3kDYVusolMsaOXu16eZ+Xa/JyJK6tpfKeu3G61iklQUWXjBs//nGCdDrX6+HKHx9ewmkDu+kPNORXpBDCBPR0G7q7Hb7JqarC9HSR/M2v3WWQkqCiS8aFF17wWLbMZ6LDvvWqTA+f2HcH/cEEXeUSKRAD+AG8+JJH7GhIPUDcOJHK+36Fd+iguxAy7qnokqIXRXDbbQkSCfAd7rTz5wfuIhkNkvYd3jYpUoYCH9JpaG1x+GYnDMFaKu+8XQNT5aSp6JKit3y5z549Hi637Tw9tZPL236vQagijoQh7D/gMTzsLkM8cSLhurUE69e5CyHjmoouKWq9vfDDHyaor7fuVvSs5a9e/BYRAbE2tRZxwhvZHujllx2etjwPW11N1e234rT6k3FLRZcUtZ/9LGRgAKeDUN/f8Shn9W/TIFQRxxIJ6Ow09Pa4W2a0tbV47W0kf7/IWQYZv1R0SdHas8fw0EMhjY3uaMOPcgAAFhpJREFUMlRm+/jLl24lFdSreV7EMQP4Hrz4osG6bKqf0Ejlz3+KaWtzF0LGJRVdUpSiCG6/PYnvQxC4y3HVgXuojPoY9h3urC0ixwQBDA0bWlsdvglKJCCKqPqX76mpXk6Iii4pSg88ELB9u0djo7sXtHN6NvInrb9R87xIkQlD2LfPY3DAXYZ40iTCp54kfOJxdyFk3FHRJUVn717Dj36UYOJEd83zFdl+/mb3Aoa8SmLj8FKbiLyBNzKqftcuz90yo+cR19dR9d1vY9rbHYWQ8UZFlxSVTAa+8Y0knud28vzHX76D+swRBoJ6dyFE5C0lQhgYMBw86HCZsbIKMzxM1a3f1DKjjIqKLikqv/51wIsvek73V3x319N84PC/0R1OdpZBRI4vkYADBz1S/e4Kr3jyZMIN60n8+xJnGWT8UNElRWPHDo9f/crtsmJ1ppv/f88/MeDXYjWTS6SoGQOeB7t2GeLIXYh4wgSqvn+HtgiS41LRJUVhaAi+/vUEiYQlDB2FsJa/eunbVGb7GApqHIUQkRMRBjA0ZNi/3+HpLFkBNqbq2/+cu/Va5C2o6JKi8KMfhbS0uN3q55Ijy7i4YwU9WlYUGVcSCWhpMfQ4HJoaN04k2LqV5G//1VkGKX4qusS55ct9HnggZNIkd31cp6d28p9f/Bp9wQSs0beFyHhiDPgB7NzpcG9GY4gnTaTyJz8i2PisoxBS7HR2Ead27fL4P/8nyYQJFt9RC1V1ppu/3fE/iQhI+w73GxKRkxb4EMew4wXPXX9XmMDWVFP91X/EaznkKIQUMxVd4kx3N3z5y0mCwFJR4SaDF2f5r7u+RF36CP3hBDchRCQvEiEMDBpefNEDRxfObXUNDA9T/Y//CwYH3YSQoqWiS5zIZuFrX0vS1QX1DkdhfXT/DzivZz3d4RR3IUQkbxIJOHLE7TZBdtIk/Jdfouq272h+l7yGii5x4sc/DnnuOY/JDnvWL+pYwZxDv6ArMUWbWYuUCAOECdj7skdvr8v5XVNIPLacxOL7nWWQ4qOiSwpu+XKfRYtCJk1yV+uckdrBf9n9T/QFDdrmR6TEeAZ8Pzf7b2jIVQiPuHEiVT+4k+DZ9Y5CSLFR0SUF9cwzPv/7fydpaLAEjmqdpsG93LD9OiJ80n6VmxAiMqYCH2wM27f7pNOOQiQS2Joaqhf8L/zt2xyFkGKioksKZvNmj3/6pyS1te4a5xuHW7hh++cI4mFSocOhYCIy5sIQMml4frtHNuMmg62uhjCk5pb/if/ibjchpGio6JKC2LnT40tfSlJRYalydHGpNtPJDds/R3W2l75wopsQIlJQiQQMDhmef8EjyrrJYGtrIY6puelGvIMH3ISQoqCiS8bcyy8bbr45ie9DjaPddSqzfVy//XoahtvoCSe5CSEiTiQTkEoZduz0iGM3GWxDAwwNUXPT/8C0t7sJIc6p6JIxdfCg4aabKshmoa7OTYaKbD9/+8LfMW3wRXoSGg0hUo6SCejtMexyWXg1NuJ1dVF70//AdHS4CSFOqeiSMbNrl8f111cwMGCY4GjuaG26g7/b9hne0b+NrrDJTQgRKQrJJHR1G1543t1SYzxpEl5bK7XXf1ZLjWVIRZeMiY0bPW68MUkUwYQJboYDTho6wE1b/ytNg3vpSjRpFpeI5K549Rm2bfPIOLqrMZ40CdPTTe3nP4u/a6ebEOKEii7Ju5UrfW65pYJEwt2S4hmpHdy05dPUZjq1pCgir5FMwOCgYctWd3O8bGMjZLPU3vg5bZBdRlR0Sd5YC7//fcA//3OSujpLdbWbHDO7n+ULW/87QZzRXYoi8qYSCchmDFu3+KRSbjLYunpsIkHNLTcRPvG4mxBSUCq6JC8GB+E730nw/e8naGx0NIfLWv7kwC/4/AvXkzEJzeESkbcVhrk3i1u3+rQfNk42ybbVNcR1dVR/bQGVd/8LZBwNFJOC0P4ncsr27zd85StJ9u/3aGqyeA5K+apsL/9591d5b/dquoOJRF6i8CFEZNwJAohj2LPHo7fP8o6zYjy/wCEqKognTSb52/sItm6h/x8XYl1uTCtjRle65JSsWuXzt39bQWuru4LrjNQObmn+FLO6n6YzOU0Fl4icEM+DRBLa2x32eQUBcVMT/o4XqPvMfyHYtMFBCBlrKrrkpKRS8H//b8jChUkqKqCxsfDX5T0bcUXLIm7a+mmqM726Q1FETpoh12A/PGRobvY5csTBcqMxxJMng4WaL36B5L0/xt3GkTIWjLXWzf38byGTiejuHhjzr9PQUFWQr1OK1q3z+O53k3R2GiZNsviFvhQPTBvYw6f2fI0Z/dvpCyeS8ZIAGM9g46J6Skue6NiWpmI8rlEM2QzU11vOPjsm6aJHNZPB6+wgPu10Ul/8EtF7ZjkIcWrK5Tw7eXLtqB+roktGrbMTvv/9BE88EVBdbakd/fMsb8J4mCsP/JQrD/2MGJ/ecOJrrm4V4wu45IeObWkq5uOaTgMGzjwzZmqTxThYGzI93ZjBIYY/ejVD/+0z2BoHL7wnqVzOsyq6RqFcngz5kMnA8uU+P/xhgqEhmDSJwvduWcv53Wv5q73fYuJQCz3hJCIvfMPDivkFXE6Njm1pKvbjGse518CqKss7zrbU1jrIGkV4R9qx9Q0MfP7vyHzwQw5ehE9cuZxnVXSNQrk8GU5FHMOTT/rcdVeC9vbcoNPKysLneEdfMx9/+Xu8o38rQ14lA0H9Wz622F/A5eTp2Jam8XJcMxmILTTUW848M6bKwRxCk+rH9PcTnTmDwc9eR/aP/rio+1jL5TyromsUyuXJcDKshfXrPe66K8G+fR7V1ZaamsLnOC21i4/tu5Pze9aSNSF9QeNxX2DGywu4nDgd29I0no6rBTLp3I8TGy3Tz4wLP5PQWkxvL2ZokOj8dzPwmc8RXfDeAocYnXI5z6roGoVyeTKciOFhWL3a59e/Dtm3z1BZCbW1hX0jZWzMO3vW86eHfsn5veuJjE9PMJHRNlOMpxdwOTE6tqVpPB7XVxdfjY2WaVNHlh0LedHJWkx3FyadJnveuxj+q0+RufQDucFjRaJczrMqukahXJ4Mo3HkiOHhh31+97uQgYHcEmJNTWGLrWQ2xfs7lnPlwXtpTLcSEdIbThh1sXXUeHwBl9HRsS1N4/m4vrr4qqy0nH66pbGxwPMKrcX09GDSw9j6Bob+8v8jPfcqbN1bt2EUSrmcZ1V0jUK5PBneyuAgrF/v88gjARs2+MSxpaEBksnCZfDiLOf0beY/HHmY9x9ZThCnGfRrGPKrT7riG88v4PL2dGxLU6kc12w2N2rC92DSZMukSZbamgJf/RocwOvtA98n84HLGZ77EbIXvy+30aQD5XKeVdE1CuXyZHi1oSHYts1j2bKA1at9stnclej6ego2a8uzEdP7n+d9HSu4tH0JFVEKsPQFjW96N+KJKpUXcHkjHdvSVGrHNbavbJ8YhjBlSszERktVFYUrwLLZ3KiJ2GLDkPSH55D58Byy73pPLlSBlMt5VkXXKJTDk8FaaGkxbN7s88QTPs8953P0aE+YYAu29F+b6eTc3k28t3Mls7pWE8ZpPBvRHzaQ8fLbhVpqL+DyCh3b0lTKxzWKc1fADLk3thMac8uPdbUWv1CtV5k0Xnc3GIP1fLKX/BGZD/4nMhe9b8z3dyyH8yyo6BqVUnwyxDHs22fYudNj0yafTZt8OjsN1kIymRtmOuZXtKylMd3K9P4XOLd3E+f3rKVp6GUshhifVFA3pnsjlvILeLnTsS1N5XJc45ECDHI9YFWVuZaO2jpLTbUlLMQKYDabu/MxmwUscdNUMhdfQvaii4nOO5/4tNPz2sxbiufZN6OiaxTG+5Mhk4GDBw379nns2uWxbZvHzp0emUyuyPJ9qK62JJNj1xBvbEzjcAtTB1/itIHdnNP3HGf1b6Mq20uMh2ctg0ENQ15Vwbryy+UFvBzp2JamcjyuFoii3IcZ+TwRQnVN7i7IqqpcY34ywdgtSVoLQ4N4/amRBAZbUUF03vlk33MB0bkzic6cQTzttJO+I3K8n2dHK+9F16pVq/j6179OHMd88pOf5DOf+cxrfj+dTvPFL36Rbdu20dDQwG233cYZZ5wBwF133cX999+P53l8+ctf5oMf/ODbfi0VXa9Ip6Gjw3DkiKGtLVdg7dmT+7GtzcPzwFpLHBsqK3PfqPleMvTiLLWZThrSh5mYbmXy4D5OH9jFtIEXmTx8AGMt1kAQZxn2Khnyq8iahLOBfeX4Al4udGxLk45rThznirA4Hrlp2+ZeRisqcq/tVdWWimTuZqdEwhKEY/Aym0ljBgYxQ4O8sv5piZqmEc84i+icc4mmTydumoadNIm4ceLb9oiNh/NsPpxI0XXcU3QURSxcuJCf/vSnNDU1MX/+fGbPns2555577DGLFi2irq6O5cuXs3TpUm699VZuv/12du/ezdKlS1m6dCltbW18+tOfZtmyZfgudkguAtbmCqn+fkMqBamUoafH0NsLXV2Gw4dzH62tHu3thv5+QxBYjDFEEceWCSsqYNKkV9+WfGIvWH6coTLqz31k+6jK9lKT7aE600XjcCsT0m1MHG6hcbiVmmwXMR6x8fFsjGcjMl6StJekN2jEmvI8liIi+eR5b9zZJ3fOMAwN5Ub7GMOxS2OWXL2TTOauiL26GAuD3BtwP7AEfm7lY1TTd8IEtj6BrX/VuIk4xvT3EmxcT/j06lxI3z9WJdq6OuIpTcRNU4mnTCGe0oSdMIG4rgFOm4xnA2xNDba6xtldlMXkuEVXc3MzM2bMYPr06QDMmzePFStWvKboeuyxx7j++usBmDt3LgsXLsRay4oVK5g3bx6JRILp06czY8YMmpubufjii8fonzN6qRR0d+eeN3Fsjr3DOLrufvTzbNYc+zybhUwm93k6nVviy2QM6XRuBMPAgCGVMgwOQn9/rqhKpQwDA4aBARgczH2do99ciXiQRDyIjSwmjgi8mGSQJRlEnBlEJGojArJ4NsK3WXybwbcR/lCWYCBDYDMEcZogThPaNGE0RGW2n4o4RUW2n8ooRWXUT0WUGvkYoCLqJ4zTRMbHGg9L7i3V0a8RERB5IRkTkvWSdIZTi3qbCRGRUmVMrr55s+sUFrBx7rwykMrdNYl9VWH2qgda+0qt5Ps292PAsYLslY/c7xmTe3zuRx9jqjFeNWakU8QzgLEYLF42g7evBe/FfXjZLH6Uyf1lgY8X+tTFUS5clIUgxFZXY6uqsFXVuY+amtznlZXY6hpsdTVUVGCTFRAG2DABYZj7MfAhCLBBOPI1AqznjVSYuX+ENSP/UC/3n2fDBLlbR4vDcYuutrY2pk6deuzzpqYmmpub3/CYadOm5f7CIKC2tpauri7a2tq48MILX/Nn29ra3vbr+b6hoWFs/4OOHIFPfcojm63Bjjwhjy6yvvrz13/E8dv/eDxh+NorsbfumU9gM5iRK1UGi7EWyD2ZX/+5x8jPbYw30pr+2p8fP0RkAtLmaAgLRLlfxxAx8utRlpAsIYOj+e8sLkcbJKT06NiWJh3XgrDZNz9PRRw9C7yKGanbRoq3o++73/D2+w2/7gHJY78QBDHnnAP4Bvww18Df2ZH7eDuGXDFlRq5QeCb386MVnzn64eW+1rEfR36do78PBCHR4geK5irbcYuuN2v5Mq+78vFWjxnNn329KLJjvgYcBHDffVV0drpdazaDP8Nk0gX9mh5QuCkthVdXV0Vvb+n3EJQjHdvSpONauiZPrqJz2O2xtUEIA9ncxxjJa0/X1KlTaW1tPfZ5W1sbU6ZMecNjWlpamDp1Ktlslr6+PhoaGkb1Z12prc0tGTpVXwUUz2XPUlDfUIXtLo53NJJfOralSce1dFU0VDGkY/sax22tu+CCC9i7dy/79+8nnU6zdOlSZs+e/ZrHzJ49m8WLFwOwbNkyLr30UowxzJ49m6VLl5JOp9m/fz979+7lve8tzt3QRURERMbSca90BUHAggULuPbaa4miiGuuuYaZM2dyxx13MGvWLD784Q8zf/58br75ZubMmUN9fT233XYbADNnzuSqq67iIx/5CL7vs2DBgrK9c1FERETKm4ajSknRcS1dOralSce1dJXLsT2Rnq7RTO4QERERkVOkoktERESkAFR0iYiIiBSAii4RERGRAlDRJSIiIlIAKrpERERECkBFl4iIiEgBqOgSERERKQAVXSIiIiIFoKJLREREpABUdImIiIgUgIouERERkQJQ0SUiIiJSACq6RERERApARZeIiIhIAajoEhERESkAFV0iIiIiBaCiS0RERKQAVHSJiIiIFICx1lrXIURERERKna50iYiIiBSAii4RERGRAlDRJSIiIlIAKrpERERECkBFl4iIiEgBqOgSERERKQAVXcCPf/xjzjvvPDo7O11HkTz49re/zZ/92Z/x0Y9+lOuuu47e3l7XkeQUrFq1irlz5zJnzhzuvvtu13EkT1paWvjrv/5rrrrqKubNm8fPfvYz15Ekj6Io4mMf+xif/exnXUcpKmVfdLW0tPD0009z2mmnuY4ieXLZZZexZMkSHnroIc466yzuuusu15HkJEVRxMKFC7nnnntYunQpS5YsYffu3a5jSR74vs8tt9zCww8/zG9+8xt+/etf69iWkJ///Oecc845rmMUnbIvur75zW9y8803Y4xxHUXy5PLLLycIAgAuuugiWltbHSeSk9Xc3MyMGTOYPn06iUSCefPmsWLFCtexJA+mTJnCe97zHgBqamo4++yzaWtrc5xK8qG1tZWVK1cyf/5811GKTlkXXStWrGDKlCmcf/75rqPIGPnd737HFVdc4TqGnKS2tjamTp167POmpiadmEvQgQMHeP7557nwwgtdR5E8+MY3vsHNN9+M55V1ifGmAtcBxtrf/M3fcOTIkTf8+o033shdd93FT37yEwep5FS93XH90z/9UwB+8IMf4Ps+f/EXf1HoeJInb7ZLma5Kl5ZUKsUNN9zAP/zDP1BTU+M6jpyixx9/nMbGRmbNmsXatWtdxyk6JV903XvvvW/66zt27ODAgQNcffXVQO5y6Cc+8QkWLVrE5MmTC5hQTsZbHdejFi9ezMqVK7n33nt1kh7Hpk6d+prl4ba2NqZMmeIwkeRTJpPhhhtu4KMf/ShXXnml6ziSBxs3buSxxx5j1apVDA8P09/fz0033cStt97qOlpR0IbXI2bPns39999PY2Oj6yhyilatWsW3vvUtfvnLX+p4jnPZbJa5c+dy77330tTUxPz58/nud7/LzJkzXUeTU2St5e///u+pr6/nS1/6kus4MgbWrl3LT37yE93M9Colf6VLys/XvvY10uk0n/70pwG48MILWbhwoeNUcjKCIGDBggVce+21RFHENddco4KrRGzYsIEHH3yQd77zncdWHL7whS/woQ99yHEykbGjK10iIiIiBaBbC0REREQKQEWXiIiISAGo6BIREREpABVdIiIiIgWgoktERESkAFR0iYiIiBSAii4RERGRAlDRJSIiIlIA/w/uufZTA02uZwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_pdfs(effect_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When the duration of `HTR` is large, effect size of `HTR` with `EVSMA_EWMA` is visible, when it is small, it is little less, but this is significant."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "---\n",
    "-----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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